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WO2017090805A1 - Procédé et dispositif pour déterminer un modèle de calcul d'aire de section transversale de muscle squelettique d'un sujet sur la base d'un facteur démographique et d'un facteur cinématique - Google Patents

Procédé et dispositif pour déterminer un modèle de calcul d'aire de section transversale de muscle squelettique d'un sujet sur la base d'un facteur démographique et d'un facteur cinématique Download PDF

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WO2017090805A1
WO2017090805A1 PCT/KR2015/012899 KR2015012899W WO2017090805A1 WO 2017090805 A1 WO2017090805 A1 WO 2017090805A1 KR 2015012899 W KR2015012899 W KR 2015012899W WO 2017090805 A1 WO2017090805 A1 WO 2017090805A1
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model
demographic
kinematic
skeletal muscle
sectional area
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Korean (ko)
Inventor
이창형
최영아
김철민
정덕영
김병철
신명준
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University Industry Cooperation Foundation of Pusan National University
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University Industry Cooperation Foundation of Pusan National University
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    • 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
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS

Definitions

  • the present invention relates to determining the size of a subject's skeletal muscle, and in particular, the skeletal muscle cross-sectional area of the subject based on demographic factors such as the age of the subject and kinematic factors such as muscle strength of the subject muscle even without an imaging device such as an MRI.
  • the present invention relates to determining a skeletal muscle cross-sectional area calculation model capable of calculating.
  • a database of information as well as resources that can be prescribed and controlled is a common reference library and is available from many sources and provides physicians with detailed information about possible disease states, information on how to recognize such conditions, and the treatment of such conditions.
  • Typical prescribable data sources include simple blood tests, urine tests, written results of physical tests, and the like.
  • determining the extent of aging plays an important role in assessing the health of the patient.
  • the composition of thigh soft tissues in young adults is used to evaluate muscle performance, and the composition of thigh soft tissues in the elderly is considered to be an important factor in determining morbidity, disorder, and mortality.
  • changes in the thigh cross-section and thigh cross-sectional changes that occur during the aging process, along with changes in the waist and hips, have been used to monitor obesity and muscle strength.
  • indicators of thigh cross-sectional area will provide a more in-depth understanding of human physiological factors related to various clinical conditions, quality of life, and overall mortality.
  • MRI has been used as a method for measuring muscle cross-sectional area and body components, but the conventional MRI method has the following limitations in measuring actual muscle size.
  • the thighs serve as the most powerful levers in the human body, and the thighs also contain vast amounts of muscle. As muscle ages, however, the cross-sectional area of the muscles decreases, and fat tissue penetrates between and between the muscles.
  • the two-point Dickson MRI technique can be used. Research using this two-point Dixon technique is important in preparing accurate indicators of muscle distribution / characteristics of the general population.
  • MRI provides a photographic image of the lesion, but the patient is exposed to radiation during imaging, which is not only expensive but also takes a long time to shoot.
  • DXA dual-energy X-ray absorptiometry
  • isokinetic strength test are used, which are relatively inexpensive and are resistant to radiation exposure. Low risk
  • these two methods have been certified for reliability in young and old groups.
  • the provision of muscle distribution / characteristic index according to the characteristics of the group is expected to play a big role in promoting the public health by identifying the clinical problems of muscle reduction in an aging society.
  • another object of the present invention is to provide a kinematic model that can predict the lean TCSA of each participant by providing personal data about the participant's muscle quality and performance, such as isokinetic strength testing. To provide a method and apparatus for the same.
  • One aspect of the present invention for achieving the above object is directed to a method for determining a model for calculating skeletal muscle cross-sectional area of a subject based on demographic and kinematic factors.
  • the method includes dividing a subject's population into a modeling population for determining the output model and a verification population for verifying the obtained output model; Calculating a skeletal muscle cross-sectional area of the subjects belonging to the modeling population and the verification population; A demographic model for determining a demographic component of a subject belonging to the modeling population and obtaining a demographic model for calculating skeletal muscle cross-sectional area of the subject from the demographic component by applying a regression analysis method to the determined demographic component.
  • Obtaining a statistical model Obtaining a kinematic model for determining the kinematic factors of the subject belonging to the modeling population, and obtaining a kinematic model for calculating skeletal muscle cross-sectional area of the subject from the kinematic factors by applying a regression analysis method to the determined kinematic factors ; Estimating skeletal muscle cross-sectional area by applying demographic and kinematic factors of a subject belonging to the test population to the obtained demographic and kinematic models; And a verification step of verifying the demographic model and the kinematic model by comparing the estimated skeletal muscle cross-sectional area with the calculated skeletal muscle cross-sectional area.
  • the step of calculating the skeletal muscle cross-sectional area obtaining the skeletal muscle image of the subject using dual-energy X-ray absorptiometry (DXA); Dividing the obtained skeletal muscle image into muscle, fat, and bone regions; And calculating the skeletal muscle cross-sectional area using the divided area for each area.
  • the demographic model obtaining step includes determining at least a dominant element of gender, age, height, and weight of the subject; And acquiring the kinematic model through multiple regression analysis while giving more weight to the determined dominant element.
  • the obtaining of the kinematic model may include determining at least a dominant element of lean body mass (LBM) and muscle strength of the subject; And acquiring the kinematic model through multiple regression analysis while giving more weight to the determined dominant element.
  • the verifying step includes the steps of: calculating a multiple decision coefficient (R 2 ) and a standard error of estimate (SEE) of the estimated demographic model and the estimated kinematic model; And determining whether the calculated multiple decision coefficient (R 2 ) and the estimated standard error (SEE) satisfy a predetermined criterion; And determining that the demographic model and the kinematic model are verified if the predetermined criterion is satisfied.
  • the thigh muscle mass is used as a measure to estimate the degree of aging of the subject.
  • Such a device may include a skeletal muscle cross-sectional area calculator for calculating skeletal muscle cross-sectional area of subjects in the population; Determine demographic elements of subjects belonging to the modeling population to determine the output model and apply regression techniques to the determined demographic elements to calculate the skeletal muscle cross-sectional area of the subjects from the demographic factors.
  • Demographic model acquisition unit for obtaining a model
  • a kinematic model acquisition unit for determining a kinematic component of a subject belonging to the modeling population and obtaining a kinematic model for calculating skeletal muscle cross-sectional area of the subject from the kinematic component by applying a regression analysis method to the determined kinematic component.
  • a skeletal muscle cross-sectional area estimator for estimating skeletal muscle cross-sectional area by applying demographic and kinematic factors of a subject belonging to a test population for verifying the obtained output model to the obtained demographic model and kinematic model
  • And a verification unit for verifying the demographic model and the kinematic model by comparing the estimated skeletal muscle cross-sectional area with the calculated skeletal muscle cross-sectional area.
  • the skeletal muscle cross-sectional area calculation unit obtains the skeletal muscle image of the subject using a dual energy X-ray absorptiometry (DXA), divides the obtained skeletal muscle image into muscle, fat, and bone regions, and calculates the divided areas. And calculate the skeletal muscle cross-sectional area.
  • the demographic model acquiring unit is configured to determine at least the dominant element among the subject's gender, age, height, and weight, and obtain the kinematic model through multiple regression analysis while giving more weight to the determined dominant element. do.
  • the kinematic model acquiring unit determines at least the dominant component of the subject's lean body mass (LBM) and muscle strength, and obtains the kinematic model through multiple regression analysis while giving more weight to the determined dominant component. It is composed.
  • LBM lean body mass
  • the verification unit calculates the multiple decision coefficient (R 2 ) and the estimated standard error (SEE) of the estimated demographic model and the estimated kinematic model, and calculates the calculated multiple decision coefficient (R 2 ) and estimated standard error (SEE). Is determined to satisfy a predetermined criterion, and if the predetermined criterion is satisfied, the demographic model and the kinematic model are determined to be verified.
  • the average muscle value of a group of subjects having demographic information such as gender, age, height, and weight and sharing similar characteristics can be predicted using a demographic model.
  • the present invention provides personal data on the quality and performance of the participants' muscles, such as DXA or isokinetic strength tests, so that, unlike the demographic model, the lean TCSA of each participant is kinematic model. Can be predicted using
  • the present invention by measuring the muscle aging and physical health through the measurement of muscle mass and area, it is possible to calculate the indicators for predicting the Korean reference and individual muscle cross-sectional area, DXA and living body By using the Impedance, the whole body or partial measurement is compared with the reference value, and the physical health index and the index of the aging process can be predicted.
  • FIG. 1 is a flowchart schematically showing a method of determining a skeletal muscle cross-sectional area calculation model according to one aspect of the present invention.
  • FIG. 2 is a system diagram illustrating an exemplary operating environment of a method for determining a skeletal muscle cross-sectional area calculation model according to one aspect of the present invention.
  • FIG. 3 is a diagram illustrating skeletal muscle cross-sectional areas of subjects obtained using 2-point Dickson MRI.
  • FIG. 4 is a graph illustrating cross-sectional areas of thigh bone, fat, and muscle measured by two-point Dickson MRI of total thigh fat.
  • FIG. 5 is a graph illustrating the difference between the pure thigh muscle cross-section predicted and actually measured using the demographic model versus the mean pure thigh muscle cross-section measured using the demographic and kinematic models.
  • FIG. 6 is a graph illustrating the difference between the pure thigh muscle cross-section predicted and actually measured using the kinematic model versus the mean pure thigh muscle cross-section measured using the demographic and kinematic models.
  • FIG. 7 is a block diagram schematically showing an apparatus for determining a model for calculating a skeletal muscle cross-sectional area according to another aspect of the present invention.
  • FIG. 8 is a computer configuration diagram showing a hardware architecture showing a computing system capable of implementing the apparatus for determining a skeletal muscle cross-sectional area calculation model according to another aspect of the present invention.
  • FIG. 1 is a flowchart schematically showing a method of determining a skeletal muscle cross-sectional area calculation model according to one aspect of the present invention.
  • the method for determining a skeletal muscle cross-sectional area calculation model determines a skeletal muscle cross-sectional area calculation model of a subject based on demographic and kinematic factors.
  • the population of the subject is divided into a modeling population for determining the calculation model and a verification population for verifying the obtained calculation model (S110).
  • the population is divided into modeling population and validation population. Modeling populations are used to model skeletal muscle cross-sectional models, and validation populations are used to verify the suitability of the modeled results. As described above, in the present invention, not only the skeletal muscle cross-sectional area calculation model is acquired, but also objectivity can be ensured by directly verifying the suitability of the obtained model.
  • the skeletal muscle cross-sectional area of the subjects belonging to each population is calculated (S120). All conventional medical image processing techniques can be used to calculate skeletal muscle cross-sectional area.
  • a two-point Dickson MRI consisting of two 16-element body array coils and spinal coils can be applied to a three tesla scanner (Magnetom Verio, Siemens Healthcare, Er Weg, Germany) to obtain thigh body composition images of participants.
  • a three tesla scanner Magnetictom Verio, Siemens Healthcare, Er Weg, Germany
  • Skeletal muscle cross-sectional area calculation model can be applied to determine all the skeletal muscle cross-sectional area of the human body, but for the convenience of understanding the cross-sectional area of the thigh muscles is illustrated. Note, however, that this does not limit the invention and that a computational model may be applied to determine various skeletal muscle cross-sectional areas.
  • FIG. 2 is a system diagram illustrating an exemplary operating environment of a method for determining a skeletal muscle cross-sectional area calculation model according to one aspect of the present invention.
  • Embodiments may be implemented in a commercial MRI system.
  • 2 shows an example of an MRI system 200, including an MRI real time control sequencer 250, and a data acquisition and display computer 280.
  • the MRI system 200 includes an XYZ magnetic gradient coil and associated amplifier 220, a fixed Z-axis magnet 210, a digital RF transmitter 260, a digital RF receiver 270, a transmit / receive switch 230. And RF coil (s) 240.
  • the MRI system 200 may be controlled in real time by the control sequencer 250 to generate magnetic and radio frequency fields that cause magnetic resonance in vivo in a patient to be imaged.
  • the contrast-enhanced image of the patient's ROI may be displayed on the display screen.
  • the display screen may be implemented through various output interfaces such as a monitor, a printer, or a data storage device.
  • two axial cross-sectional groups were designated 14.5 cm below the knee joint and 17.5 cm above the knee joint to include the largest perimeter of the lower extremities and thighs.
  • a matrix image of 380 x 380 and a field of view of 400 x 400 mm 2 was used, which produced the resulting image with a resolution of 1 mm in the plane and a cross-sectional thickness of 4 mm.
  • Images of the two-point Dickson MRI can be derived and displayed via PACS workstations (Marosis, Marotech, Seoul). Each TCS AMRI region is distinguished using the ROI-curve method. The cross-sectional area of each area was measured as follows.
  • the total thigh muscle cross section is automatically measured by following the edge of the image through the ROI-curve method.
  • the cross-sectional area of the thigh bone is automatically measured by following the ROI-curve along the edge of the gray area in the center of the image.
  • TCSA MRI of fat full TCSA MRI -darkest part of the CSA image of the thigh muscle: including thigh bone) + CSA penetrating between the inside and inside of the muscle)
  • the cross-sectional area of skeletal muscle was measured by one skilled practitioner.
  • the ICC was 0.92 ⁇ 0.96 for the measured area of interest.
  • rapid correlation was derived by one expert measuring the image of the part twice.
  • the demographic factors of the subjects belonging to the modeling population are determined, and by applying a regression analysis method to the determined demographic factors, a demographic model is obtained for calculating the skeletal muscle cross-sectional area of the subjects from the demographic factors (S130). ).
  • the demographic factors may include the subject's gender, age, height, weight, etc., and these factors are assigned different weights according to their influence on determining the skeletal muscle cross-sectional area calculation model. This weight can be determined through multiple regression analysis.
  • a kinematic model is obtained (S140). That is, the kinematic factors of the subjects belonging to the modeling population are determined, and a kinematic model for calculating skeletal muscle cross-sectional area of the subjects is obtained from the kinematic factors by applying a regression analysis method to the determined kinematic factors.
  • the kinematic element may include a subject's lean body mass (LBM) and muscle strength.
  • Dual-energy X-ray absorptiometry (DXA) scans can be used to determine these kinematic factors, and the subject remains in a supine position. The subject does not move during the scan, and likewise it is desirable for one skilled practitioner to perform the measurements.
  • the lean body weight (LBM DXA ) value of each participant's dominant leg may be used.
  • a constant velocity muscle test can be used to measure muscle strength.
  • Isokinetic strength test is widely used as a method of quantitatively measuring muscle force by measuring muscle rotational force of a joint at a constant speed.
  • Biodex ® Biodex Corporation, New York, USA
  • an isokinometer can be used to measure the maximum rotational force of the muscle groups of the lower extremities.
  • the participant is seated in a state of comfortably fixing the legs and back to the chair by using a strap, and is guided to grab the handle located in front of the participant's chest to prevent the movement of the upper limb and the hip joint. If movement of the upper limbs or hips is observed, the data should be excluded.
  • the axis In order to measure the thigh muscle (Biodex) the axis is located in the anterior superior iliac spine of the pelvis. The degree of leg movement is adjusted according to individual maximum flexion and extension, and warm-up exercises performed before the actual measurement, in which all participants bend and stretch five times at 60 ° / sec. In addition, participants perform maximum leg stretch at a rate of 60 ° / sec. Then, the biometer (Biodex) measured the maximum rotational force (Nm) and the maximum rotational force (PT / Bwt; Nm / kg) based on the weight.
  • Biodex ® used in the present invention is because the reliability of the device is high.
  • the present invention is not limited to such a device, and various devices for measuring muscle strength may be used.
  • it is possible to further check the test-retest reliability in order to improve the reliability of the skeletal muscle cross-sectional area calculation model, it is possible to further check the test-retest reliability, and the participants are preferably tested several times at regular time intervals.
  • Results intraclass correlation coefficient is preferably performed showed a 0.915 ⁇ 0.956, Biodex ® isokinetic exercise also measured by one skilled experts.
  • the skeletal muscle cross-sectional area is estimated by applying the demographic and kinematic factors of the subjects belonging to the test population to the obtained demographic model and the kinematic model (S150). .
  • the demographic model and the kinematic model are verified by comparing the estimated skeletal muscle cross-sectional area with the calculated skeletal muscle cross-sectional area (S160).
  • calculate the multiple decision coefficient (R 2 ) and the standard error of estimate (SEE) of the estimated demographic model and the estimated kinematic model and compare the error to meet certain criteria. It may be determined whether or not (S170). If the error satisfies a predetermined criterion, the obtained skeletal muscle cross-sectional area calculation models may be determined to have sufficient reliability, and the skeletal muscle cross-sectional area calculation model may be determined as a final demographic model and a final kinematic model (S180). However, if the error does not meet the criteria, the skeletal muscle cross-sectional calculation model is modeled from the beginning.
  • Data is expressed as mean ⁇ standard deviation (SD).
  • SD standard deviation
  • ANOVA was performed to compare the differences in the mean values of total thigh cross-sectional area, pure thigh muscle cross-sectional area, muscle strength, and lean body mass with respect to age, gender, height, and weight.
  • Stepwise multiple regression analysis attempts to derive an optimal equation for predicting TCSAMRI.
  • Muscle strength was included as an independent variable.
  • the Bland-Altman technique may be used to confirm the regression analysis result.
  • this is only an example and does not limit the present invention.
  • the Brant-Altman technique the difference in the mean value between the results obtained using demographic and kinematic models to explore systematic differences is plotted against the mean of pure thigh muscle cross-sectional area. (See FIGS. 3 to 6).
  • LBM DXA was used in addition to individual correlation analysis between the two models derived as described above.
  • Table 1 shows the physical characteristics of 92 participants.
  • the mean thigh area was greater in male participants (23268.38mm 2 ⁇ 4770.37mm 2 ) than in female participants (22086.73mm 2 ⁇ 4221.47mm 2 ) and by participants before age 40 (22012.17mm 2 ⁇ 4034.13mm 2 ). Greater than three participants (21347.43 mm 2 ⁇ 2426.04 mm 2 ).
  • FIG. 3 is a diagram illustrating skeletal muscle cross-sectional areas of subjects obtained using 2-point Dickson MRI.
  • Fat, muscle, and bone tissue are represented in different shades. Pure muscles are the darkest, fat tissue is the brightest, and bone tissue is the medium. In the middle of the image, the light and dark center is the bone marrow.
  • the left side (A) of FIG. 3 shows the thigh cross section of a young male, and the right side B shows the thigh cross section of an elderly female.
  • the image of such an image is analyzed and the cross-sectional area of the thigh bone, fat, and muscle measured by the 2-point Dickson MRI of total thigh fat is shown in FIG. 4.
  • FIG. 4 is a graph illustrating cross-sectional areas of thigh bone, fat, and muscle measured by two-point Dickson MRI of total thigh fat.
  • the cross-sectional area of the total thigh fat also includes the area of fat that has penetrated between the muscles.
  • the average net thigh muscle area man participant (12532.62 mm 2 ⁇ 3062.44mm 2) a woman participant (8408.08mm 2 ⁇ 1501.56mm 2) appeared to be more than, the average net thigh muscle area in the previous three 40 participants (10525.22mm 2 ⁇ 3090.62mm 2 ) were more than participants aged 65 years or older (10525.22mm 2 ⁇ 3090.62mm 2 ).
  • TCSA MRI was used as a dependent variable, and age, sex, height, and weight were independent variables.
  • Table 2 is a multiple regression model: demographic model for predicting pure TCS from height, gender, age, and weight.
  • elongation represented 55.88% of variance (variation), which was the most influential predictor for predicting TCSA MRI .
  • gender, age and weight were also statistically significant predictors of the final model (formula) generation.
  • a demographic model may be defined to predict average muscle values of a group of subjects who share similar characteristics with obtainable demographic information such as gender, age, height, and weight.
  • TCSA MRI was used as a dependent variable
  • LBM DXA and muscle strength were used as independent variables.
  • Table 3 shows a multiple regression model for predicting pure thigh muscle cross-sectional area using LBM DXA and muscle strength Biodex as variables.
  • LBM DXA represented 67.74% variance (variation), which was the most influential predictor for predicting TCSA MRI .
  • DXA has been widely used as a method for measuring actual muscle size
  • the measurement of muscle mass through DXA includes fat and degenerative muscle cells, which includes the muscle area and the risk of error in predicting / measuring size. .
  • Table 4 shows demographic model, kinematic model, and correlation between LBM DXA and muscle strength by age and gender.
  • the kinematic model provides personal data about the participant's muscle quality and performance, such as DXA and isokinetic strength tests, which, unlike demographic models, can predict the lean TCSA of each participant. .
  • the R 2 difference between the model generation sample and the model validation sample was between -0.01 and 0.01 for the demographic model and -0.002 to 0.003 for the kinematic model.
  • the predicted residual sum of square (PRESS) analyzed the modified R 2 and SEE to measure the accuracy of the predictive model.
  • the PRESS R 2 and SEE values in the demographic model were 0.78 and 1382.98, and the PRESS R 2 and SEE values in the kinematic model were 0.88 and 972.02.
  • 5 and 6 illustrate the difference between the net pure thigh muscle cross-sectional area estimated using each model and the actual net thigh muscle cross-section measured using the demographic model and kinematic model, respectively. It is a graph.
  • the broken line represents the regression line and the dotted line represents the 95% confidence interval.
  • muscle strength is higher than the demographic model and the kinematic model than the LBM DXA value. Showed.
  • FIG. 7 is a block diagram schematically showing an apparatus for determining a model for calculating a skeletal muscle cross-sectional area according to another aspect of the present invention.
  • Skeletal muscle cross-sectional area calculation model determining apparatus 700 is skeletal muscle cross-sectional area calculation unit 710, demographic model acquisition unit 720, kinematic model acquisition unit 730, control unit 750, skeletal muscle cross-sectional area estimation unit 760, and a verification unit 770.
  • the population is divided into a modeling population for determining the output model and a validation population for verifying the determined output model. Then, the skeletal muscle cross-sectional area calculation unit 710 calculates skeletal muscle cross-sectional area of the subjects belonging to the population.
  • the demographic model acquisition unit 720 determines demographic factors of subjects belonging to the modeling population to determine the output model, and applies demographics to the determined demographic elements by applying a regression analysis technique. Obtain a demographic model for calculating skeletal muscle cross-sectional area of subjects from the historical factors. In this case, the demographic model is
  • the kinematic model acquisition unit 730 determines the kinematic factors of the subjects belonging to the modeling population, and applies the regression analysis method to the determined kinematic factors to determine the skeletal muscle cross-sectional area of the subjects from the kinematic factors. Obtain a kinematic model for calculation. In this case the kinematic model is
  • the skeletal muscle cross-sectional area estimator 760 determines the demographic elements of the subject belonging to the test population for verifying the obtained output model in the obtained demographic model and kinematic model. And the skeletal muscle cross-sectional area is estimated by applying kinematic factors, and the verification unit 770 compares the estimated skeletal muscle cross-sectional area with the calculated skeletal muscle cross-sectional area to verify the demographic model and the kinematic model.
  • these models can be used as a measure to calculate thigh muscle mass to estimate the subject's degree of aging.
  • FIG. 8 is a computer configuration diagram showing a hardware architecture showing a computing system capable of implementing the apparatus for determining a skeletal muscle cross-sectional area calculation model according to another aspect of the present invention.
  • Computer system 800 includes a computer 850 (CPU), system memory 810, and a system bus 890 that couples the CPU 850 and memory 810.
  • Computer system 800 also includes a mass storage device 860 for storing program modules 870.
  • the program module 870 may operate to perform various operations and may include web server applications and imaging applications.
  • the computer includes a data storage unit 880 for storing data that may include imaging related data such as image acquisition data, and modeling for storing imaging modeling data or other types of data used to implement the present invention. It may include a data storage.
  • the mass storage device 860 is connected to the CPU 850 through a mass storage controller connected to the bus 890. Mass storage device 860 and associated computer storage media serve as a nonvolatile storage device for computer system 800.
  • computer storage media includes volatile and nonvolatile removable and non-removable media implemented in any method or technology for storage of information such as computer storage instructions, data structures, program modules, or other data. can do.
  • computer storage media may include RAM, ROM, EPROM, EEPROM, flash memory, or other semiconductor memory technology, CD-ROM, Digital Versatile Disk (DVD), HD-DVD, BLU-RAY, or other optical media.
  • computer system 800 may operate in a network configuration environment using a logical connection technique to a remote computer via network 840.
  • Computer system 800 may connect to network 840 via a network interface device 830 connected to bus 890. It should be understood that network interface device 830 may also be used to connect to other forms of network and remote computer systems.
  • Computer system 800 may also include an input / output controller 820 for receiving input from multiple input devices and processing the same.
  • Computer 850 is capable of reading code and / or data from mass storage 860 or other computer storage media via bus 890.
  • Computer storage media may be devices in the form of storage elements implemented using any suitable technology (eg, semiconductor, magnetic materials, optics, etc., but not limited to those).
  • program module 870 including an imaging application may include software instructions, which, when loaded into computer 850 and executed therein, 800) to obtain a skeletal muscle cross-sectional area calculation model.
  • the program module 870 may also provide a variety of tools or techniques in which the computer system 800 may engage within the entire system or operating environment using the components, operational flows, and data structures described throughout this specification. can do.
  • program module 870 when called and executed by computer 850, converts computer 850 and the entire computer system 800, which is a general purpose computing system, into a special purpose computing system.
  • computer 850 may be comprised of any number of transistors or other individual circuit elements, which may be in any number of states individually or collectively.
  • the computer 850 may operate as a finite-state machine in response to an execution command included in the program module 870.
  • Such computer and executable instructions may transform computer 850 by specifying how computer 850 transitions between states.
  • the transistors or other individual hardware elements constituting the computer 850 may be converted.
  • Encoding the program module 870 may also transform the physical structure of the computer storage medium. The specific transformation of the physical structure may depend on various factors in various embodiments of the description of the present invention.

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Abstract

La présente invention concerne un procédé et un dispositif pour déterminer un modèle de calcul d'aire de section transversale de muscle squelettique d'un sujet sur la base d'un facteur démographique et d'un facteur cinématique. Le procédé comprend : une étape de division d'une population de sujets en une population de modélisation pour déterminer un modèle de calcul et une population de vérification pour vérifier un modèle de calcul acquis ; une étape de calcul d'une aire de section transversale de muscle squelettique de sujets appartenant à la population ; une étape de détermination d'un facteur démographique de sujets appartenant à la population de modélisation, et l'application d'un procédé d'analyse de régression au facteur démographique déterminé de manière à acquérir un modèle démographique pour calculer une aire de section transversale de muscle squelettique des sujets à partir du facteur démographique ; une étape de détermination d'un facteur cinématique des sujets appartenant à la population de modélisation, et l'application du procédé d'analyse de régression au facteur cinématique déterminé de manière à acquérir un modèle cinématique pour calculer l'aire de section transversale de muscle squelettique des sujets à partir du facteur cinématique ; une étape d'estimation de l'aire de section transversale de muscle squelettique par application, au modèle démographique et au modèle cinématique acquis, d'un facteur démographique et d'un facteur cinématique de sujets appartenant à la population de vérification ; et une étape de vérification pour vérifier le modèle démographique et le modèle cinématique par comparaison de l'aire de section transversale de muscle squelettique estimée à l'aire de section transversale muscle squelettique calculée. Selon la présente invention, un modèle démographique et un modèle cinématique peuvent être définis de manière à prédire des paramètres musculaires moyens de groupes cibles ayant des informations démographiques pouvant être acquises et partageant des caractéristiques similaires.
PCT/KR2015/012899 2015-11-26 2015-11-30 Procédé et dispositif pour déterminer un modèle de calcul d'aire de section transversale de muscle squelettique d'un sujet sur la base d'un facteur démographique et d'un facteur cinématique Ceased WO2017090805A1 (fr)

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KR10-2015-0166438 2015-11-26

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KR102085812B1 (ko) * 2018-04-10 2020-03-06 가톨릭관동대학교산학협력단 촬영 이미지 기반의 건강상태 분석 및 정보 제공 방법, 그의 장치 및 그의 기록 매체
KR102390119B1 (ko) * 2019-12-26 2022-04-25 한양대학교 산학협력단 Lstm 딥러닝 예측 모델을 이용한 요속 예측 시스템 및 방법

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