[go: up one dir, main page]

WO2025186025A1 - Outils, ressources et détermination de durée d'intervention thérapeutique médicale, et dispositifs, sytems et procédés associés - Google Patents

Outils, ressources et détermination de durée d'intervention thérapeutique médicale, et dispositifs, sytems et procédés associés

Info

Publication number
WO2025186025A1
WO2025186025A1 PCT/EP2025/054826 EP2025054826W WO2025186025A1 WO 2025186025 A1 WO2025186025 A1 WO 2025186025A1 EP 2025054826 W EP2025054826 W EP 2025054826W WO 2025186025 A1 WO2025186025 A1 WO 2025186025A1
Authority
WO
WIPO (PCT)
Prior art keywords
medical
data
user input
procedure
intervention
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/EP2025/054826
Other languages
English (en)
Other versions
WO2025186025A8 (fr
Inventor
Thomas Erik AMTHOR
Michael Günter HELLE
Falk Uhlemann
Dirk Schaefer
Steffen Renisch
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Koninklijke Philips NV
Original Assignee
Koninklijke Philips NV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips NV filed Critical Koninklijke Philips NV
Publication of WO2025186025A1 publication Critical patent/WO2025186025A1/fr
Publication of WO2025186025A8 publication Critical patent/WO2025186025A8/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the subject matter described herein relates to optimization and planning of medical interventions for a patient using information about the patient and/or information about doctor(s). For example, a predictive network analyzes medical information for a given patient case (e.g., medical images, medical image reports, medical records, etc.), interventionalist preferences and characteristics, and available medical resources to generate an intervention plan.
  • a predictive network analyzes medical information for a given patient case (e.g., medical images, medical image reports, medical records, etc.), interventionalist preferences and characteristics, and available medical resources to generate an intervention plan.
  • Any given medical intervention e.g., surgeries, cancer treatment, medical imaging, etc.
  • the choice of an intervention method and tools necessary for performing that intervention depends on the knowledge, preferences, and experience of the interventionalist, but also on external requirements, such as guidelines or availability of resources (e.g., operating room space and availability, equipment availability, other medical personnel with required skills, etc.).
  • intervention planning can prove difficult.
  • intervention planning may require disparately housed information that is often not available or accessible to a user seeking to plan an intervention.
  • An intervention planning request is received from a user, i.e., medical professional/interventionalist.
  • One or more intervention plans and durations are generated by a predictive network, such as a neural network.
  • the predictive network may also generate numerous probabilities and indexes reflecting the suitability and importance of various tools and resources for an intervention.
  • the user may then select a particular method for an intervention, and based on the selection, the intervention planning tool may automatically generate a checklist. For example, a doctor may seek an intervention plan for inserting a stent into an artery.
  • the intervention planning tool will generate various methods indicating various preferred catheters and other tools for the stent placement procedure.
  • the intervention planning system will also provide an estimate of the time it will take to complete the stent placement procedure.
  • This intervention planning system disclosed herein has particular, but not exclusive, utility for planning an intervention and predicting the likely duration of said intervention.
  • the intervention planning system receives an intervention planning request and automatically generates predictions for intervention methods and likely duration.
  • the intervention planning system advantageously allows a user to select subsets of the datasets based on the similarity or difference from the user. This allows experienced interventionalists to receive intervention planning system predictions that conform to their standard intervention methods and less experienced interventionalists to receive intervention methods informed by other interventionalists as trained into the intervention planning system.
  • a system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions.
  • One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.
  • the present disclosure is directed to a computer-implemented method.
  • the computer-implemented method also includes receiving, from a user input device, a first user input identifying a medical professional; retrieving, from one of more databases, first data representative of the medical professional in response to the first user input; receiving, from the user input device, a second user input identifying a patient; retrieving, from the one or more databases, second data representative of the patient in response to the second user input; retrieving, from the one or more databases, third data associated with a medical facility at which the medical professional can perform a therapeutic procedure on the patient; pre-processing the first data, the second data, and the third data to form a structured data set; providing the structured data set as an input to a predictive network; generating, as an output of the predictive network, a procedure plan for the therapeutic procedure, where the procedure plan that may include at least one of a medical tool or a medical resource to be utilized by the medical professional to perform the therapeutic procedure; and outputting, to a display,
  • implementations may include one or more of the following features.
  • the method may include generating, as an output of the predictive network, a predicted duration of the therapeutic procedure.
  • the procedure plan that may include a series of substeps, and where the predicted duration that may include a plurality of sub-step durations, one sub-step duration for each sub-step in the procedure plan.
  • the procedure plan that may include a plurality of probabilities, each probability in the plurality of probabilities associated with the medical tool or the medical resource, where each probability represents a likelihood of use of the medical tool or the medical resource in the therapeutic procedure.
  • the procedure plan that may include a one or more indexes of criticality, each index of criticality associated with the medical tool or the medical resource, where each index of criticality represents the importance of the medical tool or the medical resource in the outcome of the therapeutic procedure.
  • the receiving, from a user input device, a first user input identifying a medical professional further that may include: receiving, from a user input device, a selection of a subset of medical professionals, and where the structured data is restricted to data associated with the subset of medical professionals.
  • a large language model preprocesses the first data, the second data, and the third data to form a structured dataset.
  • the first data that may include biographic information about the medical professional, where the second data that may include medical records of the patient, and where the third data that may include at least one of available medical tool or available medical resources at the medical facility.
  • implementations may include one or more of the following features.
  • the system where the processor circuit is further configured to: generate, as an output of the predictive network, a predicted duration of the therapeutic procedure.
  • the procedure plan that may include a series of sub-steps, and where the predicted duration that may include a plurality of sub-step durations, one sub-step duration for each sub-step in the procedure plan.
  • the procedure plan that may include a plurality of probabilities, each probability in the plurality of probabilities associated with the medical tool or the medical resource, where each probability represents a likelihood of use of the medical tool or the medical resource in the therapeutic procedure.
  • the procedure plan that may include a one or more indexes of criticality, each index of criticality associated with the medical tool or the medical resource, where each index of criticality represents the importance of the medical tool or the medical resource in the outcome of the therapeutic procedure.
  • the processor circuit is further configured to: receive, from a user input device, a selection of a subset of medical professionals, and where the structured data is restricted to data associated with the subset of medical professionals.
  • a large language model preprocesses the first data, the second data, and the third data to form a structured dataset.
  • the first data that may include biographic information about the medical professional, where the second data that may include medical records of the patient, and where the third data that may include at least one of available medical tool or available medical resources at the medical facility.
  • the present disclosure is directed to a non-transitory machine- readable medium that may include a plurality of machine-executable instructions which.
  • the non-transitory machine-readable medium also includes receiving, from a user input device, a first user input identifying a medical professional; retrieving, from one of more databases, first data representative of the medical professional in response to the first user input; receiving, from the user input device, a second user input identifying a patient; retrieving, from the one or more databases, second data representative of the patient in response to the second user input; retrieving, from the one or more databases, third data associated with a medical facility at which the medical professional can perform a therapeutic procedure on the patient; pre-processing the first data, the second data, and the third data to form a structured data set; providing the structured data set as an input to a predictive network; generating, as an output of the predictive network, a procedure plan for the therapeutic procedure, where the procedure plan that may include at least one of a medical tool or a medical resource to
  • implementations may include one or more of the following features.
  • the non-transitory machine-readable medium where the one or more processors are further caused to perform operation may include: generating, as an output of the predictive network, a predicted duration of the therapeutic procedure.
  • the procedure plan that may include a series of sub-steps, and where the predicted duration that may include a plurality of sub-step durations, one sub-step duration for each sub-step in the procedure plan.
  • the procedure plan that may include a plurality of probabilities, each probability in the plurality of probabilities associated with the medical tool or the medical resource, where each probability represents a likelihood of use of the medical tool or the medical resource in the therapeutic procedure.
  • Figure l is a schematic diagram of a networked system for intervention planning, according to aspects of the present disclosure.
  • Figure l is a schematic diagram of a processor circuit, according to aspects of the present disclosure.
  • Figure 3 is a schematic diagram of a deep learning network configuration, according to aspects of the present disclosure.
  • Figure 4 is a schematic diagram of at least a portion of an intervention planning system, according to aspects of the present disclosure.
  • Figure 5 is a schematic sequence diagram of a process for intervention planning, according to aspects of the present disclosure.
  • Figure 6 is a schematic diagram of at least a portion of system for training a predictive network in an intervention planning tool, according to aspects of the present disclosure.
  • Figure 7 is a schematic sequence diagram of a process for training a predictive network in an intervention planning tool, according to aspects of the present disclosure.
  • Figure 8 is a schematic diagram of a patient database, according to aspects of the present disclosure.
  • Figure 9 is a schematic diagram of an interventionalist database, according to aspects of the present disclosure.
  • Figure 10 is a schematic diagram of a resource database, according to aspects of the present disclosure.
  • Figure 11 is an example display for an intervention planning request, according to aspects of the present disclosure.
  • Figure 12 is a schematic diagram of a sequence of interfaces based on predictions of the intervention planning system, according to aspects of the present disclosure.
  • Figure 13 is a schematic diagram of slot planning support system, according to aspects of the present disclosure.
  • Figure 14 is a schematic flow diagram of a method for intervention planning, according to aspects of the present disclosure.
  • Figure 15 is a schematic flow diagram of a method for training a predictive network in an intervention planning system, according to aspects of the present disclosure.
  • an intervention planning system that automatically generates intervention plans. This may allow, for example, users (including medical professionals and other interventionalists) to select between a number of possible methods for an intervention. Furthermore, the system provides an automatic way to implement user preferences for preferred level of experience used to generate intervention plans.
  • accessing can include querying, retrieving, sorting, etc.
  • the systems and methods disclosed herein provide a number of benefits. For a given interventional procedure to be scheduled, experienced interventionalists may want to stick to their workflows, while inexperienced interventionalists may benefit from receiving guidance from more experienced colleagues. Thus, the systems and methods disclosed herein allow for an interventionalist to select proposal preferences for the preferred level of experience to make intervention predictions.
  • Figure 1 is a schematic diagram of a networked system 100 for intervention planning, according to aspects of the present disclosure.
  • the networked system 100 may for example may be used to receive an intervention planning request from a user. An intervention planning request may be transmitted to different components of the network system 100 to facilitate intervention planning.
  • the networked system 100 may provide a user with a prediction in the form of a therapeutic plan which was output from a predictive network.
  • the networked system 100 is used for generating a therapeutic plan.
  • the networked system 100 may include a network/cloud computers 110,140, user computers 160, medical imaging console 170, and medical imaging device 180.
  • the network/cloud computers 110, 140 may be in communication with each, each sending and/or receiving data and information from the other.
  • User computer 160 is depicted in Figure 1 as in communication with network/cloud computer 110. However, in some instances, user computer 160 may be in communication with either or both of network/cloud computers 110, 140. User computer 160 may be in communication with a medical imaging console 170 which is in communication with a medical imaging device 180. As described herein, communication between the different components may be accomplished by any numbers of connections, e.g., wired and/or wireless.
  • Network/cloud computer 110 may include a processor 112, input device 114, display 116, communication interface 117, and memory 118.
  • Processor 112 may include a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.
  • the processor 112 may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
  • the processor 112 is configured to process the instructions stored in memory 118.
  • the processor 112 is connected to the communication interface 117.
  • Input device 114 allows a user to make selections or provide instructions to the network/cloud computer 110.
  • the input device may be a mouse, touch screen, touch pad etc.
  • the display 116 is coupled to the processor 112.
  • the display 116 may be a monitor or any suitable display.
  • the display 116 is configured to display intervention planner 150 output, data structuring network 155 output, slot planning support 126 output, information in databases 120, 122, 124, network performance, or any other system diagnostic information.
  • the communication interface 117 is coupled to the processor 112.
  • the communication interface 117 may include one or more transmitters, one or more receivers, one or more transceivers, and/or circuitry for transmitting and/or receiving communication signals.
  • the communication interface 117 can include hardware components and/or software components implementing a particular communication protocol suitable for transporting signals to other devices in the networked system 100.
  • the communication interface 117 can be referred to as a communication device or a communication interface module.
  • the memory 118 is coupled to the processor 112.
  • the memory 118 may be any suitable storage device, such as a cache memory (e.g., a cache memory of the processor 112), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, solid state drives, other forms of volatile and non-volatile memory, or a combination of different types of memory.
  • a cache memory e.g., a cache memory of the processor 112
  • RAM random access memory
  • MRAM magnetoresistive RAM
  • ROM read-only memory
  • PROM programmable read-only memory
  • EPROM erasable programmable read only memory
  • EEPROM electrically erasable programmable read only memory
  • flash memory solid state memory device
  • hard disk drives solid state drives
  • the memory 118 can be configured to store subject information, measurements, data, or files relating to a subject’s medical history, history of procedures performed, anatomical or biological features, characteristics, or medical conditions associated with a subject, computer readable instructions, such as code, software, or other application, as well as any other suitable information or data.
  • Subject information may include measurements, data, files, other forms of medical history, such as but not limited to ultrasound images, ultrasound videos, and/or any imaging information relating to the subject’s anatomy.
  • the subject information may include parameters related to an imaging procedure such as an anatomical scan window, a probe orientation, and/or the subject position during an imaging procedure.
  • the memory 118 can also be configured to store information related to the training and implementation of machine learning algorithms (e.g., neural networks) and/or information related to implementing image recognition algorithms for detecting/segmenting anatomy, image quantification algorithms, and/or image acquisition guidance algorithms, including those described herein.
  • machine learning algorithms e.g., neural networks
  • image recognition algorithms for detecting/segmenting anatomy, image quantification algorithms, and/or image acquisition guidance algorithms, including those described herein.
  • memory 118 includes data and stored instructions for modules, including slot planning support 126.
  • Slot planning support 126 may utilize predicted durations and other data included in user intervention planning requests to plan and reserve medical facility resources to schedule an intervention. For example, slot planning support 126 may reserve a room for particular period of time based on the predicted duration of an intervention and also assign staff and other medical professionals needed for the intervention. Slot planning support 126 may create instructions and orders to provision a room, e.g., an operating room, with the necessary tools and resources for an intervention as selected by an interventionalist from the output of the intervention planner 150.
  • Processor 142 may include a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.
  • the processor 142 may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
  • the processor 142 is configured to process the instructions stored in memory 148.
  • the processor 142 is connected to the communication interface 147.
  • Input device 144 allows a user to make selections or provide instructions to the network/cloud computer 140.
  • the input device may be a mouse, touch-screen, touch pad etc.
  • the display 146 is coupled to the processor 142.
  • the display 146 may be a monitor or any suitable display.
  • the display 146 is configured to display intervention planner 150 output, data structuring network 155 output, slot planning support 126 output, information in databases 120, 122, 124, network performance, or any other system diagnostic information.
  • the communication interface 147 is coupled to the processor 142.
  • the communication interface 147 may include one or more transmitters, one or more receivers, one or more transceivers, and/or circuitry for transmitting and/or receiving communication signals.
  • the communication interface 147 can include hardware components and/or software components implementing a particular communication protocol suitable for transporting signals to other devices in the networked system 100.
  • the communication interface 147 can be referred to as a communication device or a communication interface module.
  • the memory 148 is coupled to the processor 142.
  • the memory 148 may be any suitable storage device, such as a cache memory (e.g., a cache memory of the processor 142), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, solid state drives, other forms of volatile and non-volatile memory, or a combination of different types of memory.
  • a cache memory e.g., a cache memory of the processor 142
  • RAM random access memory
  • MRAM magnetoresistive RAM
  • ROM read-only memory
  • PROM programmable read-only memory
  • EPROM erasable programmable read only memory
  • EEPROM electrically erasable programmable read only memory
  • flash memory solid state memory device, hard disk drives, solid state drives, other forms of
  • the memory 148 can be configured to store subject information, measurements, data, or files relating to a subject’s medical history, history of procedures performed, anatomical or biological features, characteristics, or medical conditions associated with a subject, computer readable instructions, such as code, software, or other application, as well as any other suitable information or data.
  • Subject information may include measurements, data, files, other forms of medical history, such as but not limited to ultrasound images, ultrasound videos, and/or any imaging information relating to the subject’s anatomy.
  • the subject information may include parameters related to an imaging procedure such as an anatomical scan window, a probe orientation, and/or the subject position during an imaging procedure.
  • the memory 148 can also be configured to store information related to the training and implementation of machine learning algorithms (e.g., neural networks) and/or information related to implementing image recognition algorithms for detecting anatomy, segmenting anatomy (e.g., defining contours of the anatomy), image quantification algorithms, and/or image acquisition guidance algorithms, including those described herein.
  • machine learning algorithms e.g., neural networks
  • image recognition algorithms for detecting anatomy, segmenting anatomy (e.g., defining contours of the anatomy), image quantification algorithms, and/or image acquisition guidance algorithms, including those described herein.
  • memory 148 includes data and stored instructions for modules, including intervention planner 150, including predictive network 152, and data structuring network 155.
  • intervention planner 150 includes predictive network 152, and data structuring network 155.
  • the intervention planner 150 and the data structuring network 155 are located on a separate network/cloud computer from databases 120, 122, 124 and slot planning support 126.
  • data from databases 120, 122, 124 may be sent to the network/cloud computer 140.
  • Intervention planner 150 may generate intervention predictions for the received data which may then be sent back to one or more of the computers and devices of networked system 100.
  • the predictive network 152 and data structuring network 155 may be any neural network-based model or rule-based programming model, e.g., as described with respect to and as depicted in Fig. 3.
  • networks 152, 155 can be Convolutional Neural Networks (CNN), decision tree models, support vector machines (SVM), generative image- to-image transformers, generative image-to-text transformers (GIT), or other transformerbased models.
  • CNN Convolutional Neural Networks
  • SVM support vector machines
  • GIT generative image-to-text transformers
  • User computer 160 may include a processor 162, input device 164, display 166, communication interface 167, and memory 168.
  • Processor 162 may include a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.
  • the processor 162 may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
  • the processor 162 is configured to process the instructions stored in memory 168.
  • the processor 162 is connected to the communication interface 167.
  • Input device 164 allows a user to make selections or provide instructions to the user computer 160.
  • the input device may be a mouse, touch-screen, touch pad, etc.
  • the display 166 is coupled to the processor 162.
  • the display 166 may be a monitor or any suitable display.
  • the display 166 is configured to display intervention predictions from intervention planner 150 as described herein.
  • the display 166 may be configured to display the images from the data generated by medical imaging device 180.
  • the communication interface 167 is coupled to the processor 162.
  • the communication interface 167 may include one or more transmitters, one or more receivers, one or more transceivers, and/or circuitry for transmitting and/or receiving communication signals.
  • the communication interface 167 can include hardware components and/or software components implementing a particular communication protocol suitable for transporting signals to other devices in the networked system 100.
  • the communication interface 167 can be referred to as a communication device or a communication interface module.
  • the memory 168 is coupled to the processor 162.
  • the memory 168 may be any suitable storage device, such as a cache memory (e.g., a cache memory of the processor 162), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, solid state drives, other forms of volatile and non-volatile memory, or a combination of different types of memory.
  • a cache memory e.g., a cache memory of the processor 162
  • RAM random access memory
  • MRAM magnetoresistive RAM
  • ROM read-only memory
  • PROM programmable read-only memory
  • EPROM erasable programmable read only memory
  • EEPROM electrically erasable programmable read only memory
  • flash memory solid state memory device
  • hard disk drives solid state drives
  • the memory 168 can be configured to store subject information, measurements, data, or files relating to a subject’s medical history, history of procedures performed, anatomical or biological features, characteristics, or medical conditions associated with a subject, computer readable instructions, such as code, software, or other application, as well as any other suitable information or data.
  • Subject information may include measurements, data, files, other forms of medical history, such as but not limited to ultrasound images, ultrasound videos, and/or any imaging information relating to the subject’s anatomy.
  • the subject information may include parameters related to an imaging procedure such as an anatomical scan window, a probe orientation, and/or the subject position during an imaging procedure.
  • the memory 168 can also be configured to store information related to the training and implementation of machine learning algorithms (e.g., neural networks) and/or information related to implementing image recognition algorithms for detecting anatomy, segmenting anatomy, image quantification algorithms, and/or image acquisition guidance algorithms, including those described herein.
  • machine learning algorithms e.g., neural networks
  • image recognition algorithms for detecting anatomy, segmenting anatomy, image quantification algorithms, and/or image acquisition guidance algorithms, including those described herein.
  • Medical imaging console 170 may include a processor 172, input device 174, display 176, communication interface 177, and memory 178.
  • Processor 172 may include a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.
  • the processor 172 may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
  • the processor 172 is configured to process the instructions stored in memory 178.
  • the processor 172 is connected to the communication interface 177.
  • Input device 174 allows a user to make selections or provide instructions to the medical imaging console 170.
  • the input device may be a mouse, touch-screen, touch pad, etc.
  • the display 176 is coupled to the processor 172.
  • the display 176 may be a monitor or any suitable display.
  • the display 166 is configured to display intervention predictions from intervention planner 150 as described herein.
  • the display 176 may be configured to display the images from the data generated by medical imaging device 180.
  • the communication interface 177 is coupled to the processor 172.
  • the communication interface 177 may include one or more transmitters, one or more receivers, one or more transceivers, and/or circuitry for transmitting and/or receiving communication signals.
  • the communication interface 177 can include hardware components and/or software components implementing a particular communication protocol suitable for transporting signals to other devices in the networked system 100.
  • the communication interface 177 can be referred to as a communication device or a communication interface module.
  • the memory 178 is coupled to the processor 172.
  • the memory 178 may be any suitable storage device, such as a cache memory (e.g., a cache memory of the processor 172), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, solid state drives, other forms of volatile and non-volatile memory, or a combination of different types of memory.
  • a cache memory e.g., a cache memory of the processor 172
  • RAM random access memory
  • MRAM magnetoresistive RAM
  • ROM read-only memory
  • PROM programmable read-only memory
  • EPROM erasable programmable read only memory
  • EEPROM electrically erasable programmable read only memory
  • flash memory solid state memory device, hard disk drives, solid state drives, other forms of
  • the memory 178 can be configured to store subject information, measurements, data, or files relating to a subject’s medical history, history of procedures performed, anatomical or biological features, characteristics, or medical conditions associated with a subject, computer readable instructions, such as code, software, or other application, as well as any other suitable information or data.
  • Subject information may include measurements, data, files, other forms of medical history, such as but not limited to ultrasound images, ultrasound videos, and/or any imaging information relating to the subject’s anatomy.
  • the subject information may include parameters related to an imaging procedure such as an anatomical scan window, a probe orientation, and/or the subject position during an imaging procedure.
  • the memory 178 can also be configured to store information related to the training and implementation of machine learning algorithms (e.g., neural networks) and/or information related to implementing image recognition algorithms for detecting anatomy, segmenting anatomy, image quantification algorithms, and/or image acquisition guidance algorithms, including those described herein.
  • machine learning algorithms e.g., neural networks
  • image recognition algorithms for detecting anatomy, segmenting anatomy, image quantification algorithms, and/or image acquisition guidance algorithms, including those described herein.
  • Medical imaging device 180 may generate medical images 185.
  • the medical imaging device is in communication with medical imaging console 170, e.g., through the communication interface 177.
  • the medical images generated by data from the medical imaging device 180 may be shown on the display 176.
  • Medical images 185 may also be communicated to any of the computers, networks/clouds 110, 140, 160.
  • aspects of the present disclosure can be implemented with medical images 185 of subjects obtained using any suitable medical imaging device 180 and/or modality.
  • medical images and medical imaging devices include x-ray images (angiographic images, fluoroscopic images, images with or without contrast) obtained by a medical imaging device such as an ultrasound imaging device, X-ray imaging device, computed tomography (CT) images obtained by a CT imaging device, positron emission tomography-computed tomography (PET-CT) images obtained by a PET-CT imaging device, magnetic resonance images (MRI) obtained by an MRI imaging device, single-photon emission computed tomography (SPECT) images obtained by a SPECT imaging device, optical coherence tomography (OCT) images obtained by an OCT imaging device, and intravascular photoacoustic (IVPA) images obtained by an IVPA imaging device.
  • the medical imaging device 180 can obtain the medical images while positioned outside the subject body, spaced from the subject body, adjacent to the subject body, in contact with the subject body,
  • Training dataset 195 may be stored on network/cloud computer 110, network/cloud computer 140, or any other network or cloud computing environment.
  • the systems described herein may be provided as a cloud service.
  • Client sites may connect to the cloud service to provide training data and to receive intervention planning services.
  • Training dataset comprises historic information, which may include past interventions, results of the interventions, patient details, interventionalist details, and tools, resources, and methods used for the intervention.
  • Training dataset 195 may comprise both structured and/or unstructured data. As described herein, unstructured data may be (pre-)processed before used as input to the model or for comparison with the output of the model.
  • FIG. l is a schematic diagram of a processor circuit 250, according to aspects of the present disclosure.
  • the processor circuit 250 may be implemented in the network/cloud computer 110, 140, user computer 160, imaging console 170, or other devices or workstations (e.g., third-party workstations, network routers, etc.), or on a cloud processor or other remote processing unit, as necessary to implement the method.
  • the processor circuit 250 may include a processor 260, a memory 264, and a communication interface 268. These elements may be in direct or indirect communication with each other, for example via one or more buses.
  • the processor 260 may include a central processing unit (CPU), a digital signal processor (DSP), an ASIC, a controller, or any combination of general-purpose computing devices, reduced instruction set computing (RISC) devices, application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other related logic devices, including mechanical and quantum computers.
  • the processor 260 may also comprise another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.
  • the processor 260 may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
  • the memory 264 may include a cache memory (e.g., a cache memory of the processor 260), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, other forms of volatile and non-volatile memory, or a combination of different types of memory.
  • the memory 264 includes a non-transitory computer-readable medium.
  • the memory 264 may store instructions 266.
  • the instructions 266 may include instructions that, when executed by the processor 260, cause the processor 260 to perform the operations described herein.
  • Instructions 266 may also be referred to as code.
  • the terms “instructions” and “code” should be interpreted broadly to include any type of computer-readable statement(s).
  • the terms “instructions” and “code” may refer to one or more programs, routines, subroutines, functions, procedures, etc.
  • “Instructions” and “code” may include a single computer-readable statement or many computer-readable statements.
  • the communication interface 268 can include any electronic circuitry and/or logic circuitry to facilitate direct or indirect communication of data between the processor circuit 250, and other processors or devices.
  • the communication interface 268 can be an input/output (I/O) device.
  • the communication interface 268 facilitates direct or indirect communication between various elements of the processor circuit 250 and/or computers of networked system 100.
  • the communication interface 268 may communicate within the processor circuit 250 through numerous methods or protocols.
  • Serial communication protocols may include but are not limited to United States Serial Protocol Interface (US SPI), Inter-Integrated Circuit (I 2 C), Recommended Standard 232 (RS- 232), RS-485, Controller Area Network (CAN), Ethernet, Aeronautical Radio, Incorporated 429 (ARINC 429), MODBUS, Military Standard 1553 (MIL-STD-1553), or any other suitable method or protocol.
  • Parallel protocols include but are not limited to Industry Standard Architecture (ISA), Advanced Technology Attachment (ATA), Small Computer System Interface (SCSI), Peripheral Component Interconnect (PCI), Institute of Electrical and Electronics Engineers 488 (IEEE-488), IEEE-1284, and other suitable protocols. Where appropriate, serial and parallel communications may be bridged by a Universal Asynchronous Receiver Transmitter (UART), Universal Synchronous Receiver Transmitter (USART), or other appropriate subsystems.
  • UART Universal Asynchronous Receiver Transmitter
  • USBART Universal Synchronous Receiver Transmitter
  • External communication may be accomplished using any suitable wireless or wired communication technology, such as a cable interface such as a universal serial bus (USB), micro USB, Lightning, or FireWire interface, Bluetooth, Wi-Fi, ZigBee, Li-Fi, or cellular data connections such as 2G/GSM (global system for mobiles) , 3G/UMTS (universal mobile telecommunications system), 4G, long term evolution (LTE), WiMax, or 5G.
  • a Bluetooth Low Energy (BLE) radio can be used to establish connectivity with a cloud service, for transmission of data, and for receipt of software patches.
  • BLE Bluetooth Low Energy
  • the controller may be configured to communicate with a remote server, or a local device such as a laptop, tablet, or handheld device, or may include a display capable of showing status variables and other information. Information may also be transferred on physical media such as a USB flash drive or memory stick.
  • FIG. 3 is a schematic diagram of a deep learning network configuration, according to aspects of the present disclosure.
  • the configuration 300 can be implemented by a deep learning network.
  • the configuration 300 includes a deep learning network 310, which may include one or more CNNs 312.
  • the CNN 312 is one example of a type of predictive model, predictive network 152 or data structuring network 155.
  • Fig. 3 illustrates one CNN 312.
  • the embodiments can be scaled to include any suitable number of CNNs 312 (e.g., about 2, 3 or more).
  • the configuration 300 can be trained for identification of various anatomy (organs, tissue, bone) and/or other features (natural and/or man-made) within a patient anatomy.
  • the configuration 300 can be further trained for segmenting human anatomy, diagnosis medical conditions or any number of other diagnostic or medical tasks.
  • the CNN 312 may include a set of N convolutional layers 320 followed by a set of K fully connected layers 330, where N and K may be any positive integers.
  • the convolutional layers 320 are shown as 320(1) to 320(N).
  • the fully connected layers 330 are shown as 330(1) to 330(K).
  • Each convolutional layer 320 may include a set of filters 322 configured to extract features from an input 302 (e.g., x-ray venogram images or other additional data).
  • the values N and K and the size of the filters 322 may vary depending on the embodiments.
  • the convolutional layers 320(1) to 320(N) and the fully connected layers 330(1) to 330(K-l) may utilize a leaky rectified non-linear (ReLU) activation function and/or batch normalization.
  • the fully connected layers 330 may be non-linear and may gradually shrink the high-dimensional output to a dimension of the prediction result 340 (e.g., location for an object detection box or other the classification output).
  • the fully connected layers 330 may also be referred to as a classifier.
  • the fully convolutional layers 320 may additionally be referred to as perception or perceptive layers.
  • the prediction result 340 may indicate a confidence score (e.g., a probability) for each class 342 based on the input image 302.
  • the classes 342 are shown as 342a, 342b, . . . , 342c.
  • the classes 342 may indicate an inguinal ligament class 342a, a crossover class 342b, a pelvic bone notch class 342c, a region of blood flow restriction class 342d, or any other suitable class.
  • a class 342 indicating a high confidence score indicates that the input image 302 or a section or pixel of the image 302 is likely to include an anatomical object/feature of the class 342. Conversely, a class 342 indicating a low confidence score indicates that the input image 302 or a section or pixel of the image 302 is unlikely to include an anatomical object/feature of the class 342.
  • the CNN 312 can also output a feature vector 350 at the output of the last convolutional layer 320(N).
  • the feature vector 350 may indicate objects detected from the input medical image 302 or other data.
  • the feature vector 350 may indicate regions associated with diseased tissue as identified from the image 302, which may be a patient medical image.
  • the feature vector 350 may indicate the pixels in a medical image associated with the location of diseased tissue, as described herein.
  • the deep learning network 310 may implement or include any suitable type of learning network.
  • the deep learning network 310 could include a convolutional neural network 312.
  • the convolutional neural network 310 may additionally or alternatively be or include a multiclass classification network, an encoder-decoder type network, a fully connected deep learning network, or any suitable network or means of identifying features within an image.
  • the fully connected neural network may transform the data not related to a medical image or it may transform data derived from an image (e.g., detected objects) generated by another network, program, or human annotator.
  • the fully connected neural network may transform information about a patient, such as age, weight, or other low-dimensional data.
  • the network may include two paths.
  • One path may be a constricting path, in which a large image, such as the image 302, may be convolved by several convolutional layers 320 such that the size of the image 302 changes in relation to the depth of the network layer.
  • the image 302 may then be represented in a low dimensional space, or a flattened space. From this flattened space, an additional path may expand the flattened space to the original size of the image 302.
  • the encoder-decoder network implemented may also be referred to as a principal component analysis (PCA) method.
  • PCA principal component analysis
  • the encoder-decoder network may segment the image 302 into patches.
  • the deep learning network 310 may include a multi-class classification network.
  • the multi-class classification network may include an encoder path.
  • the image 302 may be of a high dimensional image.
  • the image 302 may then be processed with the convolutional layers 320 such that the size is reduced.
  • the resulting low dimensional representation of the image 302 may be used to generate the feature vector 350 shown in Fig. 3.
  • the low dimensional representation of the image 302 may additionally be used by the fully connected layers 330 to regress and output one or more classes 342.
  • the fully connected layers 330 may process the output of the encoder or convolutional layers 320.
  • the fully connected layers 330 may additionally be referred to as task layers or regression layers, among other terms.
  • the deep learning network may include fully convolutional networks or layers or fully connected networks or layers or a combination of the two.
  • the deep learning network may include a multi-class classification network, an encoder-decoder network, or any combination of networks.
  • a data structuring network 155 may include a large language model (LLM).
  • LLM may be used to generate structured data from unstructured data. For example, input into the LLM could be extensive text contained in medical report written by medical professionals.
  • the LLM may generate from the unstructured medical reports structured data including diagnosis, frequency of patient interactions, response to treatment, etc.
  • LLMs may have a variety of network architectures, including autoencoders and transformer-based models.
  • Figure 4 is a schematic diagram of at least a portion of an intervention planning system 400, according to aspects of the present disclosure.
  • Intervention planning system 400 generates one or more therapeutic plans (i.e., intervention predictions 425) and predicts an expected duration 420 for the therapeutic plans.
  • User input e.g., intervention planning request 410) and selections may take the form of medical images, patient information, medical records, intervention objectives, interventionalist experience, medical resource availability (e.g., infrastructure, available scheduling time, staff resources, etc.) and the output may be a therapeutic plan 425 and the expected duration 420 of the intervention associated with the therapeutic plan.
  • a user may be any of a number of medical professionals.
  • selections by a user may cause the system 400 to automatically gather additional information based on the selections from various databases.
  • a user may also restrict predictions for therapeutic plans by interventionalist experience. For example, a user may prefer that interventions from all interventionalists be considered, interventions by interventionalists with similar experience to the user, or only interventions previously done by the user.
  • the intervention planning system 400 may include data structuring network 155, intervention planner 150, and database 415.
  • the intervention planning system 400 may receive an intervention planning request 410 from a user computer 160 or medical imaging console 170.
  • Intervention planning request 410 may comprise an interventionalist identifier and input 412, intervention objectives 414, and patient details 416.
  • interventionalist identifier and input 412 may be populated by an interventionalist using a selection interface, depicted and further described with respect to Fig. 11, allowing an interventionalist to select proposal preferences.
  • intervention objectives 414 are goals set by an interventionalist, e.g., 30% reduction in arterial blockage.
  • patient details may include identifying information of a patient so that medical records related to the patient may be retrieved from one or more databases.
  • An intervention planning request may be structured using a data structuring network 155 and serve as query to database 415 for additional patient-, interventionalist-, and/or resource-related information.
  • Data structuring network 155 may receive unstructured portions of the intervention planning request.
  • Data structuring network 155 may include one or more neural networks suitable for structuring data.
  • the input to the data structuring network may be natural language, e.g., an interventionalist descriptions of the goals for an intervention.
  • the output of the data structuring network is structured data, e.g., diagnoses conforming to a standardized format.
  • the structured data output from the data structuring network takes the form of an n-dimensional vector, wherein the location of entries in the n- dimensional vector correspond to different parts of the input context.
  • n-dimensional vector may have lower dimension, e.g., 1 to 10, whereas for more complicated contexts (e.g., a greater variety of available input data) the n-dimensional vector may have higher dimensions, e.g., greater than 10.
  • a data structuring network 155 may be a large language model (LLM).
  • LLM Large Language Model
  • An LLM may possess a Transformer architecture, which often includes a significant number of parameters (weights in a neural network). For example, LLM such as Generative Pre-trained Transformer (GPT) 3 has 175 billion parameters, Text-to-Text Transfer Transformers (T5) has around 11 billion parameters.
  • GPT Generative Pre-trained Transformer
  • T5 Text-to-Text Transfer Transformers
  • Database 415 may include a patient database 120, interventionalist database 122, or a resource database 124.
  • Information contained in intervention planning request 410 may be used to retrieve certain information from database 415.
  • patient details 416 may be used to retrieve all or a portion of a patient’s medical history contained in patient database 120;
  • interventionalist identifier and input 412 may be used to information about the interventionalist from an interventionalist database 122; or information in the intervention planning request 410 to retrieve the required information from the resource database 124, e.g., particular medical facilities and staff along with their capability and experience, respectively.
  • data structuring network 155 may be used to generate a structured form of the data from database 415.
  • patient database 120 may include medical professionals notes and/or medical reports (e.g., radiology reports) containing written language which may be processed by the data structuring network 155 into a standard format.
  • Intervention planner 150 may receive the structured data from the intervention planning request 410 and database 415. Using a predictive network 152, intervention planner 150 generates an intervention prediction 425 and an associated predicted duration 420 for the intervention prediction.
  • predictive network 152 is capable of receiving all of the input received by the intervention planner 150 and produce a multi -parametric regression as an output.
  • predictive network 152 may comprise a deep neural network comprising multiple network layers with all-to-all connections between nodes in sequential layers.
  • predictive network 152 may include one or more neural network models capable of receiving medical images, e.g., a CNN as described with respect to Fig. 3.
  • the predictive network may also be a Support Vector Machines, Random Forest model, or a decision tree model.
  • Intervention prediction 425 may comprise one or more different possible interventions, e.g., different treatment procedures using different equipment or the same equipment in a different way.
  • intervention prediction 425 may take the form as shown in Table 1, below.
  • Table 1 shows an intervention prediction 425 that proposes 3 possible methods for intervention. For each method the probability of requiring a particular tool or resource is shown in the corresponding entry of the table. For example, Method 1 has probability of 20% to require Tool 1, probability of 80% to require Tool 2, probability of 94% to require Resource 1, and a probability of 50% to require Resource 2.
  • Tools could be various pieces of medical equipment such as an intravenous catheter, and resources could be rooms of various sophistications, e.g., examination rooms or operating rooms, or various staffing requirements, e.g., a method requiring an anesthesiologist.
  • the information about a tool requirement can also be augmented by a respective use (e.g., removal of a lesion) and time (e.g., preparation of removal) or a more detailed timeline.
  • information about the underlying reasoning for a probability generated by the predictive network may be provided to a user increase acceptance of the proposed method(s) and tools.
  • the suitability of a particular method of intervention may be primarily determined by a patient’s advanced age, which rules out a number of alternative methods of intervention.
  • Intervention prediction 425 may include any numbers of methods, tools, and resources as appropriate for the context and the intervention planning request 410. For example, only one method may be possible for an intervention and so only one method would be predicted. However, for an intervention where many procedures are possible, a large number of methods may be suggested.
  • subOsteps of a method may be listed in intervention prediction 425. For example, a stent placement procedure may require postplacement imaging, which could be listed as a sub-step.
  • an index of criticality may be included in the intervention prediction 425. The index of criticality reflects how critical a certain resource is for a certain method.
  • a high index of criticality for a tool may indicate that the absence of tool has a significant impact on the outcome of the intervention.
  • an index of criticality may be generated for each tool or resource for different sub-steps/phases of the intervention.
  • the index of criticality in combination with the probability of using a certain resource may be used to prioritize or de-prioritize these critical resources in a hospital scheduling system.
  • Predicted duration 420 of an intervention may be an estimate of the time it takes to complete an intervention, e.g., a number of minutes or hours and minutes. Predicted duration 420 may be used by slot planning support 126 to reserve and schedule equipment and resources for the intervention at a medical facility, such as a hospital. In some embodiments, the estimation of the duration of the intervention further depends on the selected method, which can also include variations depending on the expertise of the interventionalist. In this case, separate estimation results are output, one for each possible method. In some embodiments, predicted duration 420 may also include an estimate of the variance (e.g., an error bar) of the duration of the planned intervention. In some embodiments, the predicted duration may include estimates of the time and/or associated variances (e.g., error bars) in the time for sub-steps/phases of the intervention.
  • the variance e.g., an error bar
  • the proposed tools and resources can be compared with the availability of tools and resources in a hospital. If required tools or resources are unavailable, the suitability of the respective methods can be adjusted accordingly or a rescheduling of the intervention if the resources are only temporarily unavailable.
  • the intervention prediction 425 or predicted duration 420 may be provided to a user computer 160 or medical imaging console 170. As further described with respect to Fig. 14, intervention prediction 425 may be provided to an interventionalist through a user interface, allowing the interventionalist to select between the proposed methods of intervention. Selections by an interventionalist, along with the predicted duration, may be provided to a slot planning support 126. Slot planning support 126 may be used by an organization to plan and reserve all the necessary infrastructure and resources for an intervention.
  • An interventionalist may provide feedback 430 on the intervention predictions 425 through user computer 160 or medical imaging console 170.
  • feedback 430 may be a quality indicator reflecting an interventionalist’ s evaluation of the suggested interventions.
  • feedback 430 may provide include intervention results to the extent they can be ascertained after completion of the intervention.
  • the feedback loop may be an automated feedback loop that collects the information about tools, resources, and durations from IT systems, or may be a manual feedback loop that acquires feedback about the intervention from the interventionalist after the procedure, or it may be a combination of the two.
  • a checklist e.g., as shown in Fig. 12, may contain the options “ready” and “not required” for each element.
  • Selections in the checklist may be recorded when the proposed tools and resources were actually used in the intervention/procedure.
  • the checklist may also include an option to add required tools or resources manually that have not been predicted by the system.
  • the tool and/or resource usage may be automatically recorded using known device/motion tracking technology, including via the use of a CNN for object detection.
  • FIG. 5 is a schematic sequence diagram of a process for intervention planning system 400, according to aspects of the present disclosure.
  • the intervention planning system can include slot planning support 126, user computer 160 or medical imaging console 170, data structuring network 155, intervention planner 150, and database 415.
  • the data structuring network 155 may receive an intervention planning request from user computer 160 or medical imaging console 170 and database 415 may be queried for information associated with the intervention planning request.
  • Database 415 may include patient database 120, interventionalist database 122, or resource database 124 as described herein.
  • step 504 data associated with the intervention planning request is retrieved from the database 415 and provided to the data structuring network 155.
  • the data structuring network 155 may (pre-)process unstructured data contained in the intervention planning request and in the data associated with the intervention planning request from database 415.
  • data from database 415 may be structured and therefore not require structuring by the data structuring network 155.
  • step 508 the structured intervention planning request and structured data from the database is provided to the intervention planner 150.
  • intervention planner 150 generates a therapeutic plan and predicted duration.
  • request 410 may be received at intervention planner 150 without any intermediate structuring by the data structuring network 155.
  • the therapeutic plan and predicted duration is provided to the user computer 160 or medical imaging console 170 and slot planning support 126.
  • step 516 user selections are provided to slot planning support 126
  • slot planning support schedules the intervention.
  • scheduling the intervention may comprise reserving various infrastructures (e.g., surgical rooms), resources (e.g., surgical tools), and staff.
  • an interventionalist may provide feedback through interaction with user computer 160 or medical imaging console 170 to the intervention planner 150.
  • steps in the process may be performed separately or concurrently and in other arrangements.
  • FIG. 6 is a schematic diagram of at least a portion of a training system 600 for a predictive network 152, according to aspects of the present disclosure.
  • Training system 600 trains the predictive network 152 to accurately predict interventions and durations for those interventions from the context provided at inference.
  • Predictive network 152 is trained by providing historic information of the choice of methods, tools, and resources, as well as the duration of intervention, alongside the respective input features described in intervention planning system 400, e.g., the contents of intervention planning request 410 and data retrieved from database 415.
  • the information about tools and methods may be extracted from reporting documents or may be recorded manually after each intervention.
  • the information about the duration of the intervention may be extracted from hospital IT systems or may be recorded manually after each intervention.
  • Training system 600 includes data structuring network 155, intervention planner 150, and model objectives/functions 615.
  • Training data 602 may be contained in the training dataset 195.
  • Training data 602 may include unstructured training data 604, structured training data 606, and ground truth labels and target predictions 608.
  • Unstructured data 604 may be in the form of natural language from medical reports, objectives for an intervention, and other sources as described herein.
  • unstructured may include images with or without annotations or classifier.
  • Structured data may include things such as patient details like sex, weight, height, medications, etc.
  • Ground truth labels and predictions 608 may include preferred methods for intervention given a context, the outcome of the preferred intervention, and/or actual duration times for the intervention.
  • a data structuring network 155 may be a large language model (LLM).
  • LLM Large Language Model
  • An LLM may possess a Transformer architecture, which often includes a significant number of parameters (weights in a neural network). For example, LLM such as Generative Pre-trained Transformer (GPT) 3 has 175 billion parameters, Text-to-Text Transfer Transformers (T5) has around 11 billion parameters.
  • GPT Generative Pre-trained Transformer
  • T5 Text-to-Text Transfer Transformers
  • Intervention planner 150 may receive the structured training data from the data structuring network 155.
  • Intervention planner 150 includes a predictive network 152, similar to the predictive network described with respect to Figs. 3 and 4. Using the predictive network 152, the intervention planner 150 generates intervention prediction and duration predictions 610 which are similar in structure and content to predicted duration 420 and intervention prediction 425 as described with respect to Fig. 4.
  • model objectives/functions 615 training system 600 compares the intervention prediction and duration predictions 610 with the associated ground truth labels and predictions 608.
  • Model objectives/functions 615 may include objectives/functions which penalize to a greater or lesser extent predicted durations which are further or closer to the ground truth labels and prediction, respectively.
  • model objectives/functions 615 may include objectives/functions that penalize selecting the wrong tool or resource with higher probability than the ground truth prediction.
  • Model objectives/functions 615 may include objectives/functions that penalize a prediction based on an incorrect ranking of methods.
  • Losses may be chosen based on the task and/or data type. Some examples of loss may be the LI and L2 loss, mean-squared error loss, cross-entropy losses, Kullback Leibler divergence loss, etc.
  • Comparisons from the model objectives/functions 615 may update parameters 620 of the predictive network 152. In some instances, updating may be accomplished using gradient of the objective functions and backpropagation to update the parameters of the predictive network 152.
  • training and/or finetuning can be done for newly introduced or experimental devices (e.g., within clinical trials).
  • the provider of a newly approved device could make the data from the tests and trials of the device available for model training, thus introducing the device into the (potential) workflow.
  • Figure 7 is a schematic sequence diagram of a process for a system for training a predictive network 600, according to aspects of the present disclosure.
  • the system for training a predictive network can include training dataset 195, data structuring network 155, intervention planner 150, and model objective/functions 615.
  • training data from training dataset 195 is provided to the data structuring network 155.
  • training data may include ground truth labels or target predictions.
  • data structuring network 155 pre-processes unstructured training data.
  • training data may be structured and not require processing by the data structuring network.
  • the structured training data 706 is provided to the intervention planner 150.
  • predictions and ground truth labels or target predictions are provided to model objectives/functions 615.
  • model objectives/functions 615 are used to compare predictions with ground truth labels or target predictions.
  • step 714 the comparisons of predictions and ground truth labels or target predictions are provided to the intervention planner 150.
  • intervention planner 150 may update network parameters based on the comparisons.
  • updating involves the use of backpropagation of model objective/function gradients to modify the parameters of a predictive network contained in intervention planner 150.
  • FIGS 8-10 are schematic diagrams of various databases. Each database may share data in common with another. For example, an interventionalist may have performed an intervention on a patient in the past and that information could be reflected both in the patient database 120 and the interventionalist database 122. Each item in these databases may be associated with a scheduling system to allow effective assignment of infrastructure, equipment, staff, and resources, avoiding double-booking.
  • FIG. 9 is a schematic diagram of an interventionalist database 122, according to aspects of the present disclosure.
  • Interventionalist database 122 may include a collection of past procedures 910 performed by one or more interventionalists (including the interventionalist who may place the intervention planning request as described with respect to Fig. 4) and interventionalist details 920.
  • Past procedures 910 may be a structured or unstructured list of historical interventions performed by an interventionalist, including the outcome resources used and other information associated with the intervention.
  • Interventionalist details 920 may include information such educations, experience, specialties, and preferences for each interventionalist.
  • FIG. 10 is a schematic diagram of a resource database 124, according to aspects of the present disclosure.
  • Resource database 124 may include facility infrastructure 1010, Equipment 1020, and staff resources 1030.
  • Facility infrastructure 1010 may include a listing of rooms and associated capabilities for one or more medical facilities. For example, operating rooms will have different capabilities compared to examination rooms. There may be many other room types, reflecting different specialized capabilities.
  • Equipment 1020 may include any number of devices, e.g., catheters, EKG, CT, MRI, etc.
  • Equipment 1020 contains the tools that may comprise part of the output of the intervention planning system 400.
  • Staff resources 1030 may including a listing of staff that includes specialties and experience.
  • FIG. 11 is an example display 1100 for an intervention planning request, according to aspects of the present disclosure.
  • Example display 1100 may be a user interface provided through a display at the user computer 160 or medical imaging console 170 to an interventionalist.
  • the display 1100 may include prompts for an interventionalist to identify a patient 1105, the intervention objective 1110, and identify the interventionalist 1115.
  • Patient slot 1105 may receive a patient identifier (e.g., by a unique identifier or name).
  • Intervention objective 1110 may be a natural language description provided by the interventionalist describing the diagnosis for the patient and the goal of the intervention.
  • Interventionalist slot 1115 may receive an interventionalist identifier (e.g., a unique alphanumeric identifier or name). Interventionalist slot 1115 may be automatically populated based on an interventionalist’ s credentials used to log in to user computer 160 or medical imaging console 170.
  • interventionalist identifier e.g., a unique alphanumeric identifier or name.
  • an interventionalist may be prompted to selected to domain of experience over which interventions may be generated by the intervention planning system, reflected by the “Choose Proposal Preferences” sub-window 1120.
  • Probabilities generated by intervention planning system 400 may depend on the interventionalist profile, since they may have different preferences or expertise.
  • the intervention planning system 400 may output separate probability sets for: (1) The interventionalist scheduled to perform the procedure (i.e., based on status or training data from the interventionalist); (2) Peers of the interventionalist scheduled to perform the procedure (i.e., based on status or training data from a set of interventionalists with similar background, experience, and preferences); or (3) All interventionalists (i.e., based on the full training data set).
  • Figure 12 is a schematic diagram of a sequence of interfaces based on predictions on the intervention planning system, according to aspects of the present disclosure.
  • a user selection interface 1205 may be generated presented to an interventionalist on a display of user computer 160 or medical imaging console 170.
  • the user selection interface 1205 includes a structured presentation of the intervention prediction 425.
  • user selection interface 1205 includes suitability percentages 1207-1209.
  • Suitability percentages 1207-1209 indicate how appropriate the method is for the planned intervention. For example, Method 1 has the highest suitability percentage of 90%.
  • an index of criticality may also be included with the probabilities for each tool and resource.
  • a preparation checklist 1210 may be generated.
  • Preparation checklist 1210 includes a list of tools and resources associated with the selected method.
  • Checklist 1210 may have boxes for marking whether a particular tool or resource is ready or not ready.
  • checklist 1210 may be used to mark whether a tool or resource was used during an intervention.
  • a completed checklist may be used to provide feedback to the intervention planning system 400 and update the predictive network as described herein.
  • Figure 13 is a schematic diagram of slot planning support system 1300, according to aspects of the present disclosure. Slot planning support system 1300 allows a medical facility or other healthcare organization to plan for interventions and reserve facility space, such as an operating room.
  • Slot planning support system 1300 includes slot planning support 126.
  • Slot planning support 126 may receive projected duration 420 and generate infrastructure and staff reservations 1305.
  • Infrastructure and staff reservations 1305 may include use of medical facility IT systems to reserve one or more rooms and to assign one or more staff to assist the intervention in that room at the scheduled time.
  • slot planning support 126 may also receive the predicted intervention. Slot planning support 126 may utilize the predictions for the likelihood of using various tools and resources to ensure the tools are available at the time of the intervention in the correct room.
  • Figure 14 is a schematic flow diagram of a method 1400 for intervention planning, according to aspects of the present disclosure. It is understood that the steps of method 1400 may be performed in a different order than shown in Figure 14, additional steps can be provided before, during, and after the steps, and/or some of the steps described can be replaced or eliminated in other aspects. One or more of steps of the method 1400 can be carried out by one or more devices and/or systems described herein, such as components of the networked system 100 and/or processor circuit 250.
  • the method 1400 includes retrieving, from one of more databases, first data representative of the medical professional in response to the first user input. For example, interventionalist details from interventionalist database 122.
  • the first data comprises biographic information (e.g., stored in interventionalist database 122) about the medical professional.
  • the method 1400 includes receiving, from the user input device, a second user input identifying a patient. For example, a user input to prompt 1105 though a user computer 160.
  • the method 1400 includes retrieving, from the one or more databases, second data representative of the patient in response to the second user input.
  • second data For example, patient information from patient database 120.
  • the second data comprises medical records (e.g., stored in patient database 120) of the patient.
  • the method 1400 includes retrieving, from the one or more databases, third data associated with a medical facility at which the medical professional can perform a therapeutic procedure on the patient. For example, resource information from resource database 124.
  • the third data comprises at least one of available medical tool or available medical resources (e.g., stored in resource database 124) at the medical facility.
  • the method 1400 includes pre-processing the first data, the second data, and the third data to form a structured data set. For example, pre-processing with the data structuring network 155 the intervention planning request 410 and data from database 415.
  • the method 1400 includes providing the structured data set as an input to a predictive network. For example, providing the output of the data structuring network 155 to intervention planner 150, containing predictive network 152.
  • method 1400 further includes restricting the structured data to data associated with the subset of medical professionals (e.g., based on a selection by a user in 1120).
  • the procedure plan 425 comprises a plurality of probabilities, each probability in the plurality of probabilities associated with the medical tool or the medical resource (e.g., the probabilities depicted in Table 1), wherein each probability represents a likelihood of use of the medical tool or the medical resource in the therapeutic procedure.
  • the procedure plan 425 comprises a one or more indexes of criticality, each index of criticality associated with the medical tool or the medical resource, wherein each index of criticality represents the importance of the medical tool or the medical resource in the outcome of the therapeutic procedure.
  • the method 1400 includes outputting, to a display, a screen display comprising at least one of the medical tool or the medical resource. For example, a display 166 on user computer 160 displaying the user selection interface 1205.
  • Figure 15 is a schematic flow diagram of a method 1500 for training a predictive network 152 in an intervention planning system, according to aspects of the present disclosure. It is understood that the steps of method 1500 may be performed in a different order than shown in Figure 15, additional steps can be provided before, during, and after the steps, and/or some of the steps described can be replaced or eliminated in other aspects. One or more of steps of the method 1500 can be carried by one or more devices and/or systems described herein, such as components of the networked system 100 and/or processor circuit 250.
  • the method 1500 includes retrieving, from one or more databases, training data, including inputs for a predictive network and ground truth outputs (an intervention plan and/or duration). For example, training data 602 retrieved from training dataset 195.
  • the method 1500 includes pre-processing the training data into structured training data. For example, preprocessing training data 602 with data structuring network 155.
  • the method 1500 includes providing the structured training data as an input to a predictive network. For example, providing the output of data structuring network 155 to the intervention planner 150, including the predictive network 152.
  • the method 1500 includes generating, as an output of the predictive network, a procedure plan for the therapeutic procedure and an expected duration, wherein the procedure plan comprises at least one of a medical tool or a medical resource to be utilized by the medical professional to perform the therapeutic procedure.
  • the intervention planner 150 including predictive network 152, generating intervention predication and durations predictions 610.
  • the method 1500 includes comparing, using objective functions and/or losses, the procedure plan and expected duration with corresponding ground truth outputs. For example, model objective s/functions 615 comparing intervention prediction and durations predictions 610 with associated ground truth labels and predictions 608.
  • the method 1500 includes updating predictive network based on the comparison. For example, using the comparison from model objectives/functions 615 update parameters 620 of the predictive network 152.
  • the technology disclosed herein is also applicable to other medical imaging modalities obtained from a medical imaging device where 3D data is available, such as other ultrasound applications, camera-based videos, X-ray videos, and 3D volume images, such as computer aided tomography (CT) scans, magnetic resonance imaging (MRI) scans, optical coherence tomography (OCT) scans, or intravenous ultrasound (IVUS) pullback sequences.
  • CT computer aided tomography
  • MRI magnetic resonance imaging
  • OCT optical coherence tomography
  • IVUS intravenous ultrasound
  • the technology described herein can be used in a variety of settings including emergency department, intensive care, inpatient, and out-of-hospital settings.
  • All directional references e.g., upper, lower, inner, outer, upward, downward, left, right, lateral, front, back, top, bottom, above, below, vertical, horizontal, clockwise, counterclockwise, proximal, and distal are only used for identification purposes to aid the reader’s understanding of the claimed subject matter, and do not create limitations.
  • Connection references e.g., attached, coupled, connected, joined, or “in communication with” are to be construed broadly and may include intermediate members between a collection of elements and relative movement between elements unless otherwise indicated. As such, connection references do not necessarily imply that two elements are directly connected and in fixed relation to each other.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Epidemiology (AREA)
  • General Business, Economics & Management (AREA)
  • Biomedical Technology (AREA)
  • Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Pathology (AREA)
  • Databases & Information Systems (AREA)
  • Chemical & Material Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Medicinal Chemistry (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

Un système de planification d'intervention reçoit une première entrée d'utilisateur identifiant un professionnel médical et une seconde entrée d'utilisateur identifiant un patient. Le système récupère des premières données représentatives du professionnel médical en réponse à la première entrée d'utilisateur ; des deuxièmes données représentatives du patient en réponse à la deuxième entrée d'utilisateur ; et des troisièmes données associées à une installation médicale au niveau de laquelle le professionnel médical peut réaliser une procédure thérapeutique sur le patient. Le système pré-traite les premières données, les deuxièmes données et les troisièmes données pour former un ensemble de données structurées et fournit l'ensemble de données structurées en tant qu'entrée à un réseau prédictif. Le système génère avec le réseau prédictif un plan de procédure pour la procédure thérapeutique. Le système délivre, à un dispositif d'affichage, un affichage d'écran comprenant l'outil médical ou la ressource médicale.
PCT/EP2025/054826 2024-03-07 2025-02-24 Outils, ressources et détermination de durée d'intervention thérapeutique médicale, et dispositifs, sytems et procédés associés Pending WO2025186025A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202463562513P 2024-03-07 2024-03-07
US63/562,513 2024-03-07

Publications (2)

Publication Number Publication Date
WO2025186025A1 true WO2025186025A1 (fr) 2025-09-12
WO2025186025A8 WO2025186025A8 (fr) 2025-10-02

Family

ID=94772005

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2025/054826 Pending WO2025186025A1 (fr) 2024-03-07 2025-02-24 Outils, ressources et détermination de durée d'intervention thérapeutique médicale, et dispositifs, sytems et procédés associés

Country Status (1)

Country Link
WO (1) WO2025186025A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180052962A1 (en) * 2015-03-10 2018-02-22 Elekta, Inc. Adaptive treatment management system with a workflow management engine
US20210151148A1 (en) * 2018-04-10 2021-05-20 Armadahealth Llc Action planner systems and methods to simulate and create a recommended action plan for a physician and a care team, optimized by outcome
WO2022256782A1 (fr) * 2021-06-02 2022-12-08 Elekta, Inc. Planification automatisée de paramètres discrets

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180052962A1 (en) * 2015-03-10 2018-02-22 Elekta, Inc. Adaptive treatment management system with a workflow management engine
US20210151148A1 (en) * 2018-04-10 2021-05-20 Armadahealth Llc Action planner systems and methods to simulate and create a recommended action plan for a physician and a care team, optimized by outcome
WO2022256782A1 (fr) * 2021-06-02 2022-12-08 Elekta, Inc. Planification automatisée de paramètres discrets

Also Published As

Publication number Publication date
WO2025186025A8 (fr) 2025-10-02

Similar Documents

Publication Publication Date Title
US12482552B2 (en) Facilitating artificial intelligence integration into systems using a distributed learning platform
US10984905B2 (en) Artificial intelligence for physiological quantification in medical imaging
US20210264212A1 (en) Complex image data analysis using artificial intelligence and machine learning algorithms
US20210169576A1 (en) System and method for identifying comparable cases in preoperative surgical planning
US10825167B2 (en) Rapid assessment and outcome analysis for medical patients
Wehbe et al. Deep learning for cardiovascular imaging: a review
EP3404666A2 (fr) Évaluation rapide et analyse de résultats pour des patients médicaux
JP2024019441A (ja) 人工知能モデルを使用機関に特化させる学習方法、これを行う装置
US20130311472A1 (en) Imaging protocol update and/or recommender
Mazzanti et al. Imaging, health record, and artificial intelligence: hype or hope?
CN104487974A (zh) 用于将患者信息与临床标准进行匹配的系统和方法
US20220051805A1 (en) Clinical decision support
US20240006039A1 (en) Medical structured reporting workflow assisted by natural language processing techniques
WO2018073707A1 (fr) Système et procédé de recommandation d'objet de découverte structuré (sfo) sensible au flux de travail pour un continuum de soins cliniques
EP3659150B1 (fr) Dispositif, système et procédé pour optimiser le flux de production d'acquisition d'images
US20250166763A1 (en) System and method for automatically displaying information at a radiologist dashboard
WO2025186025A1 (fr) Outils, ressources et détermination de durée d'intervention thérapeutique médicale, et dispositifs, sytems et procédés associés
US20230094690A1 (en) Incorporating clinical and economic objectives for medical ai deployment in clinical decision making
Moore et al. Artificial intelligence: Clinical relevance and workflow
Samhitha et al. Disease Identification and Detection using Machine Learning
EP4379672A1 (fr) Procédés et systèmes de classification d'un ensemble de données d'images médicales
US20240404698A1 (en) Medical information processing apparatus, medical information processing method, and recording medium
US20240339214A1 (en) Method and system for optimizing medical imagining examination
YI et al. Visual Question Answer System for Skeletal Image Using Radiology Images in the Healthcare Domain Based on Visual and Textual Feature Extraction Techniques
WO2025146414A1 (fr) Images médicales pour évaluation physiologique et sélection de modèle pour analyse d'images médicales

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 25708131

Country of ref document: EP

Kind code of ref document: A1