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WO2025153965A1 - Systèmes et procédés de détermination et/ou de prédiction de complications des voies respiratoires post-opératoires aiguës associées à une chirurgie de la colonne cervicale antérieure - Google Patents

Systèmes et procédés de détermination et/ou de prédiction de complications des voies respiratoires post-opératoires aiguës associées à une chirurgie de la colonne cervicale antérieure

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Publication number
WO2025153965A1
WO2025153965A1 PCT/IB2025/050428 IB2025050428W WO2025153965A1 WO 2025153965 A1 WO2025153965 A1 WO 2025153965A1 IB 2025050428 W IB2025050428 W IB 2025050428W WO 2025153965 A1 WO2025153965 A1 WO 2025153965A1
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WO
WIPO (PCT)
Prior art keywords
poa
acute
airway
complication
probability
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/IB2025/050428
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English (en)
Inventor
Ran Harel
Gil KIMCHI
Shachar SHEMESH
David Levin
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.)
Sheba Impact Ltd
Original Assignee
Sheba Impact Ltd
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 Sheba Impact Ltd filed Critical Sheba Impact Ltd
Publication of WO2025153965A1 publication Critical patent/WO2025153965A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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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
    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/032—Transmission computed tomography [CT]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications

Definitions

  • ACS anterior cervical spine
  • Figure 1A shows a schematic block diagram illustration of a Post-Operative Airway (POA) Complications Detection and/or Prediction System, according to some embodiments.
  • POA Post-Operative Airway
  • Figure IB shows an example Graphical User Interface (GUI) for using the system, according to some embodiments.
  • GUI Graphical User Interface
  • Figure 2 depicts a flowchart of a study design, according to some embodiments.
  • Figures 3A and 3B show screenshots of three-dimensional visualization of the automated segmentation outputs, according to some embodiments.
  • Figure 4 depicts an Al Model's Training Workflow, according to some embodiments.
  • Figure 5 depicts the trained Al model confusion matrix, according to some embodiments.
  • ASC surgery may be associated with the rare yet life-threatening complication of acute postoperative airway (POA) obstruction due to edema or hematoma.
  • POA acute postoperative airway
  • Acute POA obstruction if not recognized and treated promptly can have devastating outcomes if not recognized and treated promptly.
  • the system may be configured to identify an anomaly in the patient for determining, based on the detected anomaly, a probability of a patient to experience complications in association with ASC surgeries.
  • the anomaly may be identified pre-operatively, intra-operatively, and/or post-operatively.
  • the probability of a patient to experience complications in association with ASC surgery may be determined pre-operatively, intra- operatively, and/or post-operatively.
  • a probability of a patient to experience complications in association with ASC surgery in association with a time period and/or an anatomical location of interest may be determined or identified pre-operatively, intra-operatively, and/or post- operatively.
  • the image dataset may be segmented for training the ML learning based on datasets of segmented images.
  • the segmentation of the images may be performed, for example, on osseous vertebral structures, implanted hardware, retropharyngeal space, and/or the airway.
  • the image datasets may be anonymized image datasets.
  • the image datasets may be based on one or more imaging modalities including, for example, X-ray images, and/or CT images.
  • labels may additionally pertain to additional medical, physiological, and/or socio-economic and/or behavioral patient characteristics such as, for example, gender, age, race, height, BMI, smoking habits, drinking habits, medical history, and/or the like.
  • An ML model may be trained using supervised and/or unsupervised learning.
  • the classifier may be a regression-based classifier, based on artificial neural networks (ANNs), and/or based on Gradient Boosting model.
  • the Gradient Boosting algorithm produces a prediction model that is based on an ensemble of weak prediction models (e.g., decision trees). The model is designed to solve an optimization problem that tries to minimize the difference between the model predictions on a test dataset, and the real labels on a dataset of labelled data.
  • the machine learning model may be adapted by evaluating labels produced by a test dataset.
  • the validation measures may include, for example, accuracy, recall and/or precision, with respect to real labels on a dataset of labeled data.
  • the apparatus may be configured to perform image dataset analysis using heuristics models. Further, in some instances, the machine learning and heuristics models may be combined into a hybrid model for analyzing the image dataset.
  • datasets may be excluded as training datasets, based on one more exclusion criteria.
  • the system may be configured to automatically include and/or exclude training datasets provided for training the classifier engine.
  • Exclusion criteria include, for instance, cervical spinal cord injuries graded ASIA A-C; respiratory complications necessitating mechanical ventilation unrelated to airway obstruction; and/or absence of post-operative imaging acquired during admission.
  • machine learning refers to a procedure embodied as a computer program configured to induce patterns, regularities, and/or rules from previously collected data to develop an appropriate response to future data or describe the data in some meaningful way.
  • Examples of machine learning procedures suitable for the present embodiments include, without limitation, clustering, association rule algorithms, feature evaluation algorithms, subset selection algorithms, support vector machines, classification rules, cost-sensitive classifiers, vote algorithms, stacking algorithms, Bayesian networks, decision trees, neural networks, instance-based algorithms, linear modeling algorithms, k-nearest neighbors (KNN) analysis, ensemble learning algorithms, probabilistic models, graphical models, logistic regression methods (including multinomial logistic regression methods), gradient ascent methods, singular value decomposition methods and principle component analysis.
  • KNN k-nearest neighbors
  • the machine learning procedure used according to some embodiments of the present invention is a trained machine learning procedure, which provides output that is related non-linearly to the parameters with which it is fed.
  • a machine learning procedure can be trained according to some embodiments of the present invention by feeding a machine learning training program with parameters that characterizes each of a cohort of subjects that have been diagnosed as either experiencing or not experiencing acute POA obstruction. Once the data is fed, the machine learning training program generates a trained machine learning procedure or forms a part of a ML module. In some examples, the trained ML module can be used without the need to re-train it. In some other examples, the trained ML module may be further trained and tested.
  • the system includes at least one processor; and
  • At least one memory configured to store data and software code portions executable by the at least one processor to cause to perform the following:
  • the trained ML module determines for the data, by the trained ML module, an output relating to or indicative of a comparatively high probability that the mammalian is expected to experience a severe POA-related complication.
  • the mammalian is a human subject.
  • the system may provide a physician, based on the performed analysis, with one or more intervention recommendations relating to detected acute POA-related complications.
  • interventions can include, for example, subjecting patient to ICU observation, intubation, and/or wound revision.
  • the determining may include distinguishing between:
  • the system may produce at least one first output indicative of a probability expected to develop non-acute POA obstruction.
  • At least one second output indicative of a probability expected to develop acute POA obstruction At least one second output indicative of a probability expected to develop acute POA obstruction.
  • I/O device 1100 may be employed to access data and/or information generated by the system 1000 and/or to provide inputs including, for instance, control commands, operating parameters, queries, and/or the like.
  • I/O device 1100 may allow a user of a system to receive or access medical airway images of a patient and/or other patient-related information.
  • I/O Device 1100 may interface with a Graphical User Interface (GUI) 1110, e.g., shown in Figure IB.
  • GUI Graphical User Interface
  • Processor 1200 may be implemented by various types of processor devices and/or processor architectures including, for example, embedded processors, communication processors, graphics processing unit (GPU)-accelerated computing, soft-core processors, quantum-based processor and/or general-purpose processors.
  • processors may additionally or alternatively refer to a controller.
  • Processor 1200 may be implemented by various types of processor devices and/or processor architectures including, for example, embedded processors, communication processors, graphics processing unit (GPU)-accelerated computing, soft-core processors, quantum-based processor and/or general-purpose processors.
  • GPU graphics processing unit
  • Memory 1300 may be implemented by various types of memories, including transactional memory and/or long-term storage memory facilities and may function as file storage, document storage, program storage, or as a working memory. The latter may for example be in the form of a static randomaccess memory (SRAM), dynamic random-access memory (DRAM), read-only memory (ROM), cache and/or flash memory.
  • SRAM static randomaccess memory
  • DRAM dynamic random-access memory
  • ROM read-only memory
  • cache and/or flash memory As working memory, memory 1300 may, for example, include, e.g., temporally based and/or non-temporally based instructions.
  • long-term memory memory 1300 may for example include a volatile or non-volatile computer storage medium, a hard disk drive, a solid-state drive, a magnetic storage medium, a flash memory and/or other storage facility.
  • a hardware memory facility may, for example, store a fixed information set (e.g., software code) including, but not limited to, a file, program, application
  • System 1000 may further include a power module 1600 for powering the various components and/or modules and/or subsystems of the apparatus.
  • Power module 1600 may comprise an internal power supply (e.g., a rechargeable battery) and/or an interface for allowing connection to an external power supply.
  • engine 1400 functionalities may be implemented on-premises (e.g., in a hospital or other clinical facility), and some by devices, apparatuses and/or system which are located off-premises (e.g., the "cloud”).
  • Alternative configurations may also be conceived.
  • the inventors developed novel artificial intelligence (Al) software for predicting and identifying post-operative airway obstruction at an early stage based on routine postoperative imaging.
  • the Al model designed in this study showed promising potential in predicting airway compromise following anterior cervical spine surgery, as well as discerning normal postoperative changes from rapidly deteriorating complications like edema or retropharyngeal hematoma.
  • the model demonstrated adaptability to varying radiographic environments, encompassing both radiographs and CT scans. While this tool may assist in the early detection of these life-threatening complications, further studies are necessary to validate our initial findings and integrate this modality into real-life clinical practice.
  • Acute post-operative airway obstruction following anterior cervical spine surgery is a rare but potentially life-threatening complication. If not promptly recognized and treated, it can result in devastating outcomes, including prolonged hypoxemia, anoxic brain damage, and the need for crash intubation.
  • the estimated incidence rates of this complication vary between studies, ranging between 0.7% and 2.4% of anterior spinal fusion procedures 6,16.
  • Possible etiologies for post-operative airway obstruction include retropharyngeal hematoma with or without pharyngeal edema, that may be exacerbated by vocal cord paralysis due to recurrent laryngeal nerve injury (4,10,17,20). To date, there remains a limited understanding of the established risk factors contributing to the development of this complication.
  • Previously described risk factors include increased number of surgical levels involved, administration of anticoagulants, extended surgery duration, as well as the presence preexisting conditions such as of diffuse idiopathic skeletal hyperostosis (DISH) and ossification of the posterior longitudinal ligament (OPLL) (21 16).
  • DISH diffuse idiopathic skeletal hyperostosis
  • OPLL posterior longitudinal ligament
  • FIG. 3A and 3B shows screenshots of different views of three-dimensional visualization of the automated segmentation output, showing three-dimensional reconstruction of anatomical regions, including the spine, pre-vertebral space, hardware, and airway segmented by the Al model
  • CT computed tomography
  • GUI graphical user interface
  • CatBoost Yandex, Inc. Moscow, Russia
  • a gradient boosting algorithm was utilized to process the automatically segmented voxel data from CT scans and pixel data from radiographic scans.
  • the decision trees were designed to make sequential selections between radiographs and CT scans. This flexibility was incorporated to cater to diverse clinical scenarios, accounting for instances where either a CT or radiograph were the primary post-operative imaging method.
  • the model generated and permutated multiple decision trees to address data variations. In cases where data points, like post-operative radiographs, were absent for certain patients, CatBoost diverged from the common gradient boosting approach of creating synthetic data. Instead, it treated the missing data as a distinct category, allowing for continued learning and prediction even in the presence of data gaps.
  • k- fold cross-validation approach was utilized 1, wherein the data was split into k segments and the model was subsequently trained on each. This approach aimed to bolster its dependability and curtail the likelihood of overfitting.
  • CatBoost Predicting Pending Post-Surgical Airway Obstruction
  • advanced age smoking, elevated BMI
  • preference for corpectomies over discectomies surgical interventions on segments above C5, extended operation durations, and a greater number of operated surgical segments (11).
  • the Al model designed in this study showed promising potential in predicting pending airway obstruction following anterior cervical spine surgery, while discerning normal postoperative changes from rapidly deteriorating complications like edema or retropharyngeal hematoma.
  • the model demonstrated adaptability to varying radiographic environments, encompassing both radiographs and CT scans. While this tool may assist in the early detection of these life-threatening complications, further studies are necessary to validate our initial findings and integrate this modality into real-life clinical practice. To our knowledge, this is the first publication attempting to predict airway obstruction following anterior cervical spine surgery in Al-driven tools.
  • each of the verbs, "comprise” “include” and “have”, and conjugates thereof, are used to indicate that the data portion or data portions of the verb are not necessarily a complete listing of components, elements or parts of the subject or subjects of the verb.
  • the phrase "A,B,C, or any combination of the aforesaid” should be interpreted as meaning all of the following: (i) A or B or C or any combination of A, B, and C, (ii) at least one of A, B, and C; (iii) A, and/or B and/or C, and (iv) A, B and/or C.
  • the phrase A, B and/or C can be interpreted as meaning A, B or C.
  • the phrase A, B or C should be interpreted as meaning "selected from the group consisting of A, B and C". This concept is illustrated for three elements (i.e., A, B, C), but extends to fewer and greater numbers of elements (e.g., A, B, C, D, etc.).
  • the computer program product is configured to perform the following, when run on a computer:
  • the computer program product is configured to implement, when run on a computer, an ML module that was trained with image datasets comprising images obtained through one or more imaging modalities.
  • the one or more imaging modalities comprise X-ray imaging techniques, CT imaging techniques, or both.
  • the computer program product is configured, when run on a computer, to distinguish between acute and non-acute POA-related complications.
  • the computer program product is configured, when run on a computer, to identify an anatomical location that is related to an acute and/or non-acute POA complication.
  • the computer program product is configured to implement, when run on a computer, a classifier for classifying a POA complication into one of the following classes: "acute POA complication" and "non-acute POA complication”.
  • the computer program product is configured to implement, when run on a computer, an Airway Analysis Engine.
  • Embodiments pertain to a system for detecting and/or predicting Post-Operative Airway (POA) Complications, the system comprising:
  • At least one memory configured to store data and software code portions executable by the at least one processor to cause to perform:
  • the Airway Analysis Engine determines for and/or based on the received data, by the Airway Analysis Engine, a probability that the mammalian is expected to experience a POA-related complication.
  • the system e.g., the Airway Analysis Engine
  • the Airway Analysis Engine is configured to provide an output relating to the determined probability.
  • the one or more imaging modalities comprise X-ray imaging techniques, CT imaging techniques, or both.
  • the system e.g., the Airway Analysis Engine
  • the system is configured to predict a probability that the mammalian is expected to experience acute POA-related complications.
  • the system e.g., the Airway Analysis Engine
  • the Airway Analysis Engine is configured to distinguish between acute and non-acute POA-related complications.
  • the system e.g., the Airway Analysis Engine
  • the system is configured to provide an output relating to and/or indicative of a probability that the mammalian is expected to experience an acute POA- related complication.
  • the system e.g., the Airway Analysis Engine
  • the system is configured to provide an output relating to or indicative of a probability that the mammalian is expected to experience an acute and non- acute POA-related complication.
  • the method comprises providing an output relating to the determined probability.
  • the one or more imaging modalities comprise X-ray imaging techniques, CT imaging techniques, or both.
  • the method comprises implementing an ML module by a Gradient Boosting Module.
  • the method comprises predicting a probability that the mammalian is expected to experience acute POA related complications.
  • the method comprises classifying a POA complication into one of the following classes: "acute POA complication” and "non-acute POA complication”.
  • Kernbach JM, Staartjes VE Foundations of machine learning-based clinical prediction modeling: Part ii— generalization and overfitting. Machine Learning in Clinical Neuroscience:

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Abstract

Des modes de réalisation concernent des systèmes et des procédés de détection et/ou de prédiction de complications post-opératoires des voies respiratoires (POA), le système comprenant : au moins un processeur ; et au moins une mémoire configurée pour stocker des données et des parties de code logiciel exécutables par le ou les processeurs pour amener à effectuer : la réception de données qui sont descriptives d'au moins une image acquise post-fonctionnellement de la voie aérienne d'un mammifère, la fourniture d'un moteur d'analyse des voies respiratoires avec les données reçues; et la détermination des données reçues, par le moteur d'analyse des voies respiratoires, d'une probabilité que le mammifère soit censé subir une complication liée à la POA. Des modes de réalisation et des procédés peuvent également être configurés pour fournir une sortie relative à la probabilité déterminée. Dans certains exemples, le moteur d'analyse des voies respiratoires peut comprendre un module d'apprentissage automatique (ML) entraîné pour prédire des probabilités concernant des complications POA.
PCT/IB2025/050428 2024-01-16 2025-01-15 Systèmes et procédés de détermination et/ou de prédiction de complications des voies respiratoires post-opératoires aiguës associées à une chirurgie de la colonne cervicale antérieure Pending WO2025153965A1 (fr)

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US20160278670A1 (en) * 2013-10-11 2016-09-29 Centre Hospitalier Universitaire Vaudois (Chuv) Difficult intubation or ventilation or extubation prediction system
US20200038109A1 (en) * 2017-01-12 2020-02-06 Mazor Robotics Ltd. Image based pathology prediction using artificial intelligence
US20210042916A1 (en) * 2018-02-07 2021-02-11 Ai Technologies Inc. Deep learning-based diagnosis and referral of diseases and disorders
US20210272287A1 (en) * 2020-02-28 2021-09-02 Siemens Healthcare Gmbh Method for providing airway information
CN116313053A (zh) * 2023-03-16 2023-06-23 山西医科大学 术后并发症预测模型训练方法及术后并发症预测方法

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Title
NONOMURA RYO, TABATA TOSHIHARU, SASAKI TAKANOBU, MITOMO HIDEKI, ISHIBASHI NAOYA, SUGAWARA TAKAFUMI, METOKI HIROHITO: "Prediction of Postoperative Respiratory Complications after Lobectomy in Lung Cancer Patients with COPD by Quantitative Image Analysis: A Historical Cohort Study", ANNALS OF THORACIC AND CARDIOVASCULAR SURGERY, ASCULAR SURGERY, TOKYO, JP, vol. 28, no. 6, 1 January 2022 (2022-01-01), JP , pages 411 - 419, XP093336911, ISSN: 1341-1098, DOI: 10.5761/atcs.oa.22-00133 *
TIM COOK AND MICHAEL SELTZ KRISTENSEN: "Core Topics in Airway Management", 3 December 2020, CAMBRIDGE UNIVERSITY PRESS, ISBN: 9781108419536, article HUITINK JOHANNES M., COOK TIM: "Chapter 3 - The Epidemiology of Airway Management Complications", pages: 22 - 37, XP009564112, DOI: 10.1017/9781108303477.005 *

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