Systems and methods for diagnosing sarcopenia

Abstract

A system and method for assessing sarcopenia in a patient is provided, comprising receiving longitudinal ultrasound images acquired by longitudinal scanning of a muscle of interest, receiving transverse ultrasound images acquired by transverse scanning of the muscle of interest using ultrasound imaging, segmenting the longitudinal and transverse ultrasound images to identify a target muscle region, determining a global attenuation of the target muscle region, respectively, identifying regions of interest within the target muscle region in the longitudinal and transverse ultrasound images, respectively, determining a local attenuation coefficient of the region of interest, determining a global acoustic velocity within the target muscle region in the longitudinal and transverse scan ultrasound images, respectively, determining a local acoustic velocity within the region of interest within the target muscle region, respectively, and determining a level of sarcopenia in the patient based on the global attenuation and acoustic velocity within the target muscle region and the local attenuation coefficient and acoustic velocity within the region of interest.

Description

Technical Field

[0001] Sarcopenia is a chronic disease characterized by the progressive loss of skeletal muscle mass, composition, and function in the human body. Generally, in the case of sarcopenia patients, muscle fibers are replaced by intramuscular adipose tissue (fat content). The prevalence of sarcopenia varies between 10% and 27% worldwide, and severe sarcopenia varies between 2% and 9% worldwide. Additional reported risk factors associated independently with sarcopenia include family status, lifestyle, physical inactivity, poor nutrition and dental status, and diseases (e.g., osteoporosis, metabolic diseases), although age may be the most important risk factor. In particular, the possibility of developing sarcopenia correlates with many cardiometabolic risk factors, especially diabetes, hypertension, dyslipidemia, loss of motor neurons, low-activity neuromuscular junctions, hormonal status, pro-inflammatory cytokines, reduced mitochondrial function, abnormal myokine production, and weight loss accompanied by reduced appetite. Screening and early diagnosis of sarcopenia are major medical and social issues due to the generally aging societies worldwide. In particular, ultrasonic images have emerged as an important tool for measuring muscle mass and quality. Also, ultrasonic images offer the unique advantage of being a non-invasive modality that can be used at the bedside for continuous measurements.

Background Art

[0002] Ultrasonics has been used for muscle function evaluation, including texture analysis and speed of sound (SoS) ultrasonics. For example, FIGS. 1A and 1B show transverse ultrasonic images of the proximal one-third of the rectus femoris of two female patients with similar age and body mass index (BMI). In particular, image 101 in FIG. 1A shows the rectus femoris of a 38-year-old first patient with a BMI of 24.2 who is an avid runner, and image 102 in FIG. 1B shows the rectus femoris of a 42-year-old second patient with a BMI of 25.1 who does no physical activity. For both patients, the cross-sectional area (CSA) index of the first patient is 7.575 cm 2 whereas the CSA of the second patient is 7.351 cm 2In this respect, they have similar muscle atrophy. Nevertheless, in the second patient, increased myoabdominal echogenicity may be observed due to fatty infiltration detectable in image 102. [Overview of the project] [Problems that the invention aims to solve]

[0003] However, conventional workflows for sarcopenia assessment using ultrasound imaging are based on simple parameters such as muscle thickness and echogenicity, as well as the CSA index, which presents several significant methodological problems and yields conflicting results from previous studies. For example, conventional workflows for muscle ultrasound examination are complex and time-consuming, and there are no advanced tools to help reduce the workload. Furthermore, there is no clear standardization in clinical practice. [Means for solving the problem]

[0004] In a typical embodiment, A method for diagnosing sarcopenia in a patient, wherein the method is The steps include receiving a longitudinal ultrasound image obtained by longitudinal scanning of the muscle of interest in the patient using ultrasound imaging, The steps include receiving a lateral ultrasound image obtained by a lateral scan of the muscle of interest in the patient using ultrasound imaging, The steps include segmenting the longitudinal and transverse ultrasound images to identify the target muscle region of the muscle of interest in the longitudinal and transverse ultrasound images, The steps include determining the overall attenuation in the target muscle region between the boundaries in the longitudinal ultrasound image and the transverse ultrasound image, respectively. The steps include identifying the region of interest in the target muscle region in the longitudinal ultrasound image and the transverse ultrasound image, respectively. The steps include determining the local attenuation coefficients of the region of interest in the target muscle region, The steps include determining the overall acoustic velocity in the target muscle region between the boundaries in the longitudinal ultrasound image and the transverse ultrasound image, The steps include determining the local acoustic velocity in the region of interest within the target muscle region, A step of determining the level of sarcopenia in the patient based on the overall attenuation and overall acoustic velocity in the target muscle region, and the local attenuation coefficient and local acoustic velocity in the region of interest. A method is provided that has the following characteristics.

[0005] In another representative embodiment, A system for diagnosing sarcopenia in patients, wherein the system is User interface and A processor that communicates with the aforementioned user interface, Non-temporary memory, which, when executed by the processor, the processor, The steps include receiving a longitudinal ultrasound image obtained by longitudinal scanning of the muscle of interest in the patient using ultrasound imaging, The steps include receiving a lateral ultrasound image obtained by a lateral scan of the muscle of interest in the patient using ultrasound imaging, The steps include segmenting the longitudinal and transverse ultrasound images to identify the target muscle region of the muscle of interest in the longitudinal and transverse ultrasound images, The steps include determining the overall attenuation in the target muscle region between the boundaries in the longitudinal ultrasound image and the transverse ultrasound image, respectively. The steps include identifying the region of interest in the target muscle region in the longitudinal ultrasound image and the transverse ultrasound image, respectively. The steps include determining the local attenuation coefficients of the region of interest in the target muscle region, The steps include determining the overall acoustic velocity in the target muscle region between the boundaries in the longitudinal ultrasound image and the transverse ultrasound image, The steps include determining the local acoustic velocity in the region of interest within the target muscle region, A step of determining the level of sarcopenia in the patient based on the overall attenuation and overall acoustic velocity in the target muscle region, and the local attenuation coefficient and local acoustic velocity in the region of interest. Non-temporary memory that stores the instructions to execute and A system is provided having the following: The system further includes a display configured to show an indicator of the level of sarcopenia.

[0006] In another representative embodiment, A non-temporary computer-readable medium for storing instructions for diagnosing sarcopenia in a patient, which, when executed by a processor, the processor, The steps include receiving a longitudinal ultrasound image obtained by longitudinal scanning of the muscle of interest in the patient using ultrasound imaging, The steps include receiving a lateral ultrasound image obtained by a lateral scan of the muscle of interest in the patient using ultrasound imaging, The steps include segmenting the longitudinal and transverse ultrasound images to identify the target muscle region of the muscle of interest in the longitudinal and transverse ultrasound images, The steps include determining the overall attenuation in the target muscle region between the boundaries in the longitudinal ultrasound image and the transverse ultrasound image, respectively. The steps include identifying the region of interest in the target muscle region in the longitudinal ultrasound image and the transverse ultrasound image, respectively. The steps include determining the local attenuation coefficients of the region of interest in the target muscle region, The steps include determining the overall acoustic velocity in the target muscle region between the boundaries in the longitudinal ultrasound image and the transverse ultrasound image, The steps include determining the local acoustic velocity in the region of interest within the target muscle region, Determining the level of sarcopenia in the patient based on the overall attenuation and overall acoustic velocity in the target muscle region, and the local attenuation coefficient and local acoustic velocity in the region of interest; Displaying an indicator of the level of sarcopenia; A non - transient computer - readable medium is provided that is configured to cause the above to be executed.

[0007] Exemplary embodiments are best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that the various features are not necessarily drawn to scale. In fact, the dimensions can be arbitrarily increased or decreased for the purpose of clarifying the discussion. Where applicable and practical, like reference numerals refer to like elements.

Brief Description of the Drawings

[0008] [Figure 1A] Shows a transverse ultrasonic image of the rectus femoris muscle of a first patient who exercises regularly and has low fat infiltration in the muscle. [Figure 1B] Shows a transverse ultrasonic image of a second patient who does not exercise and has high fat infiltration in the muscle. [Figure 2] It is a simplified block diagram of a system for performing sarcopenia evaluation using ultrasonic images according to an exemplary embodiment. [Figure 3A] Shows excellent muscle segmentation results by a deep - learning - based segmentation algorithm according to an exemplary embodiment. [Figure 3B] Shows moderate to poor muscle segmentation results by a deep - learning - based segmentation algorithm according to an exemplary embodiment. [Figure 4] It is a flowchart showing a method for evaluating sarcopenia in a subject using ultrasonic images according to an exemplary embodiment.

Modes for Carrying Out the Invention

[0009] In the following detailed description, for purposes of explanation and not limitation, representative embodiments are described in order to provide a thorough understanding of the embodiments according to the present teachings. Descriptions of known systems, devices, materials, methods of operation, and methods of manufacture may be omitted so as not to obscure the description of the representative embodiments. Nevertheless, systems, devices, materials, and methods within the scope of those skilled in the art are within the scope of the present teachings and may be used in accordance with the representative embodiments. It is to be understood that the terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting. Defined terms are in addition to the technical and scientific meaning of the defined terms as commonly understood and accepted in the technical field of the present teachings.

[0010] In this specification, terms such as first, second, third, etc. may be used to describe various components or constituent components, but it should be understood that these components or constituent components should not be limited by these terms. These terms are used only to distinguish one component or constituent component from another component or constituent component. Thus, a first element or component described below may be referred to as a second element or component without departing from the teachings of the concepts of the present invention.

[0011] The terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting. When used in this specification and the appended claims, the singular forms of the terms "a", "an", and "the" are intended to include both the singular and the plural unless the context clearly dictates otherwise. Additionally, the terms "comprise", "comprising", and / or similar terms specify the presence of the stated features, elements, and / or constituent components, but do not preclude the presence or addition of one or more other features, elements, constituent components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0012] Unless otherwise specified, when a component or component is said to be “connected,” “joined,” or “adjacent” to another component or component, it should be understood that the component or component may be directly connected to or joined to the other component or component, or that there may be an intermediary component or component. In other words, these terms and similar terms include cases where one or more intermediate components or components may be used to connect two components or components. However, when a component or component is said to be “directly connected” to another component or component, this includes only cases where two components or components are connected to each other without any intermediate or intermediary components or components.

[0013] Accordingly, this disclosure is intended to elicit one or more of the advantages described below specifically, through its various aspects, embodiments, and / or one or more of its particular features or sub-components. For illustrative purposes, not limiting, and to provide a complete understanding of the embodiments described herein, exemplary embodiments disclosing certain details are described. However, other embodiments consistent with this disclosure that deviate from the specific details disclosed herein are limited to the appended claims. Furthermore, descriptions of well-known apparatus and methods may be omitted so as not to obscure the descriptions of exemplary embodiments. Such methods and apparatus are within the scope of this disclosure.

[0014] In general, the various embodiments described herein provide systems and methods for performing an improved workflow for sarcopenia assessment using ultrasound imaging. The workflow includes automatically segmenting the ROI of the muscle under investigation from ultrasound imaging; measuring the overall attenuation for the entire portion of the muscle from the ROI for both longitudinal and transverse scans; measuring the local attenuation within smaller ROIs for the portion of the muscle; measuring the acoustic velocity for both longitudinal and transverse scans; and evaluating the grading of sarcopenia from the combination of measured ultrasound parameters.

[0015] This workflow reduces the time and effort required to perform musculoskeletal (MSK) examinations because data acquisition is the same as that required for conventional ultrasound examinations where muscles are clearly visible in longitudinal and transverse scans. The workflow also simplifies muscle quality assessment, especially for relatively inexperienced users (e.g., physicians and ultrasound technicians), thereby improving results and increasing user confidence. This workflow enables early diagnosis of patients at high risk of sarcopenia by using quantitative muscle ultrasound to reduce effort and examination time.

[0016] In patients with sarcopenia, muscle fibers are replaced by intramuscular adipose tissue (fat content), resulting in a decrease in acoustic velocity. This is because the acoustic velocity in adipose tissue is lower (1440 m / s) compared to that in muscle (1585 m / s). Correspondingly, the attenuation of ultrasound in ultrasound images is also reduced along with the intramuscular fat percentage. Since muscle tissue is anisotropic, certain physical properties have different values ​​when measured in different directions, and according to the embodiments herein, ultrasound parameters from both longitudinal and transverse scans are integrated into a workflow for sarcopenia assessment.

[0017] Figure 2 is a simplified block diagram of a system for performing sarcopenia assessment using ultrasound images, according to a typical embodiment.

[0018] Referring to Figure 2, System 100 includes a workstation 105 for performing and / or managing the process described herein with respect to evaluating sarcopenia of a subject 150 using ultrasound images from an ultrasound imaging device 140. The workstation 105 includes one or more processors, indicated by processor 120, one or more memories, indicated by memory 130, a user interface 122, and a display 124. Processor 120 communicates with the ultrasound imaging device 140 via an imaging interface (not shown). The ultrasound imaging device 140 includes an ultrasound transducer probe 145 that can be operated by the user to acquire partial musculoskeletal (MSK) ultrasound images of the subject 150. The ultrasound transducer probe 145 can be operated manually by the user, automatically by a robot under the control of a robot controller (not shown), or a combination of both.

[0019] Memory 130 stores instructions that can be executed by the processor 120. When executed, the instructions cause the processor 120 to implement one or more processes for performing a sarcopenia assessment of the subject 150 using ultrasound images acquired by the ultrasound imaging device 140. The ultrasound images may be provided from the ultrasound imaging device 140 in real time or near real time during the scan procedure, or retrieved from storage following the scan procedure. For illustrative purposes, memory 130 is shown to include software modules, each containing instructions that can be executed by the processor 120, corresponding to the relevant capabilities of the system 100.

[0020] Processor 120 represents one or more processing units, which may be implemented using hardware, software, firmware, hardwired logic circuits, or any combination thereof, by a general-purpose computer, central processing unit (CPU), digital signal processor (DSP), graphical processing unit, computer processor, microprocessor, state machine, programmable logic device, field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), or a combination thereof. Any processor or processing unit as used herein may include multiple processors, parallel processors, or both. Multiple processors may be contained in a single device or multiple devices, or may be combined. As used herein, the term “processor” encompasses electronic components capable of executing a program or machine-executable instructions. A processor may also refer to a collection of processors within a single computer system or distributed across multiple computer systems, such as a cloud-based or other multi-site application. A program has software instructions that are executed by one or more processors, which may be within the same computing device or distributed across multiple computing devices.

[0021] Memory 130 may include main memory and / or static memory, and such memories may communicate with each other and with the processor 120 via one or more buses. Memory 130 may be implemented by any number, types, and combinations of random access memory (RAM) and read-only memory (ROM), for example, and may store various kinds of information such as software algorithms, artificial intelligence (AI) machine learning models, and computer programs, all of which are executable by the processor 120. Various types of ROM and RAM may include any number, types, and combinations of computer-readable storage media, such as disk drives, flash memory, electrically field-programmable gate array read-only memory (EPROM), electrically erasable and field-programmable gate array read-only memory (EEPROM), registers, hard disks, removable disks, tapes, compact disc read-only memory (CD-ROM), digital versatile discs (DVDs), floppy disks, Blu-ray discs, universal serial bus (USB) drives, or any other form of storage media. Memory 130 is a tangible storage medium for storing data and executable software instructions, and is non-transient while the software instructions are stored. As used herein, the term “non-transient” should be interpreted as a characteristic of a state that persists over a period of time, rather than as a permanent characteristic of the state. The term “non-transient” specifically negates freating characteristics such as the characteristics of a carrier or signal, or other formations that exist only temporarily at any given time and place. Memory 130 may store software instructions and / or computer-readable code that enable the performance of various functions. Memory 130 may be secure and / or encrypted, or non-secure and / or unencrypted.

[0022] System 100 may also include a database 112 for storing information that can be used by various software modules of memory 130. For example, database 112 may include image data from previously acquired ultrasound images of subject 150 and / or other similarly located subjects. Stored image data can be used, for example, to train AI machine learning models such as neural network models, as discussed below. Database 112 may be implemented by any number, types, and combinations of RAM and ROM. Various types of ROM and RAM may include any number, types, and combinations of computer-readable storage media, such as disk drives, flash memory, EPROM, EEPROM, registers, hard disks, removable disks, tapes, CD-ROMs, DVDs, floppy disks, Blu-ray disks, USB drives, or any other forms of storage media known in the art. Database 112 comprises a tangible storage medium for storing data and executable software instructions, and is non-temporary while the data and software instructions are stored. Database 112 may be secure and / or encrypted, or non-secure and / or not encrypted. For illustrative purposes, database 112 is shown as a separate storage medium, but it will be understood that it may be combined with and / or contained within memory 130 without departing from the scope of this instruction.

[0023] The processor 120 includes or can access an artificial intelligence (AI) engine, which can be implemented as software that provides artificial intelligence (e.g., a neural network model) and applies machine learning as described herein. The AI ​​engine may reside in any of the various components in addition to the processor 120, such as memory 130, an external server, and / or the cloud. If the AI ​​engine is implemented in the cloud, such as a data center, the AI ​​engine may be connected to the processor 120 via the internet or other communication network using one or more wired and / or wireless connections.

[0024] The user interface 122 is configured to provide the user with information and data output by the processor 120, memory 130, and / or the ultrasound imaging device 140, and / or to receive information and data input by the user. That is, the user interface 122 enables the user to input data and control or operate aspects of the process described herein, and enables the processor 120 to indicate the effect of the user's input, which may include controlling or operating the ultrasound transducer probe 145. All or part of the user interface 122 may be implemented by a graphical user interface (GUI), such as a GUI 128 viewable on the display 124 described below. The user interface 122 may include, for example, one or more interface devices such as a mouse, keyboard, trackball, joystick, microphone, video camera, touchpad, touchscreen, or voice or gesture recognition captured by the microphone or video camera.

[0025] The display 124 may be, for example, a computer monitor, television, liquid crystal display (LCD), organic light-emitting diode (OLED), flat panel display, solid-state display, or cathode ray tube (CRT) display, or an electronic whiteboard. The display 124 includes a screen 126 for viewing an ultrasound image of the subject 150, and includes a GUI 128 that communicates to the user the degree of sarcopenia (if any) shown in the image, along with various features described herein, and allows the user to interact with the displayed image and features. In one embodiment, the ultrasound imaging device 140 may include a separate dedicated display for acquiring ultrasound images, and this dedicated display may also be represented by the display 124.

[0026] Referring to memory 130, the various modules store a set of data and instructions that can be executed by the processor 120 to perform sarcopenia assessment, as described above. The longitudinal scan image module 131 is configured to receive and process ultrasound images of the muscle of interest 155 within the subject 150 (referred to as the "longitudinal ultrasound image") provided by the ultrasound imaging device 140 from a longitudinal scan. The transverse scan image module 132 is configured to receive and process ultrasound images of the muscle of interest 155 (referred to as the "transverse ultrasound image") from a transverse scan by the ultrasound imaging device 140. The longitudinal scan generally provides a length and depth viewpoint of the muscle of interest 155 from the ultrasound transducer probe 145, and the transverse scan generally provides a width and depth viewpoint of the muscle of interest 155 from the ultrasound transducer probe 145. The transverse scan may be provided in a viewpoint substantially perpendicular to the viewpoint of the longitudinal scan, as indicated by the arrows in Figure 2. However, it should be understood that the vertical and horizontal scans can differ from each other by approximately 45 to 135 degrees without deviating from the scope of this instruction.

[0027] Longitudinal and transverse ultrasound images may be displayed on the display 124. Longitudinal and transverse ultrasound images may be received in real time or near real time from the ultrasound imaging device 140 during a simultaneous imaging session of the subject 150. The display of real-time images particularly allows the user to visualize the anatomical structure of the subject 150 while operating the ultrasound transducer probe 145. Alternatively or additionally, the longitudinal and transverse ultrasound images may be previously acquired images retrieved from a storage device (e.g., database 112) during a previous imaging session.

[0028] The segmentation module 133 receives longitudinal ultrasound images from the longitudinal scan image module 131 and transverse ultrasound images from the transverse scan image module 132 of the same muscle of interest 155, and performs segmentation of the muscle of interest 155 in each of the received longitudinal and transverse ultrasound images. Segmentation may be performed using known conventional image segmentation methods or deep learning-based segmentation algorithms, for example, as will be apparent to those skilled in the art. In one embodiment, the deep learning-based segmentation algorithm may be a convolutional neural network (CNN), such as U-Net. Alternatively or additionally, at least part of the segmentation and / or editing for automated segmentation may be performed manually by the user. In various embodiments, segmentation of longitudinal ultrasound images may be performed using the same or different methods or algorithms as those used to perform segmentation of transverse ultrasound images. Segmentation provides a first target muscle region 151 of the muscle of interest 155 from a longitudinal ultrasound image and a second target muscle region 153 of the muscle of interest 155 from a transverse ultrasound image.

[0029] In one embodiment, the deep learning-based segmentation algorithm described above can be implemented as an N-fold voting mechanism that runs, for example, N differently trained predictive models, where N is a positive integer. In one embodiment, N may be, for example, less than or equal to 10, because N greater than 10 can lead to undesirably long processing times. According to the N-fold voting mechanism, the training set and validation set are segmented equally into N folds. Then, each fold is sequentially selected as the validation set to train N predictive models using these N different data selections, while the remaining N-1 folds provide the training set. All predictive models are used to predict muscle segmentation, and the final result is voted by, for example, averaging the predicted segmentations to obtain the final result. Since the user has the opportunity to edit the boundaries of the target muscle region, as discussed below, one predictive model (N = 1) is typically sufficient to run the deep learning-based segmentation algorithm. For example, a 10-fold mechanism (N = 10) generally provides a slight improvement in the performance of deep learning-based segmentation algorithms, but requires more time and computational power to implement.

[0030] In one embodiment, the segmentation module 133 also determines the quality of the muscle segmentation in the longitudinal and transverse ultrasound images, and whether the quality is acceptable. For example, the segmentation module 133 can automatically determine the quality of the muscle segmentation by identifying which parts of the segmented ultrasound image have unusable region segmentation quality due to contours that are, for example, not smooth and / or have odd depressions. Alternatively, the segmentation module 133 may display the segmentation results on the display 124 so that the user can judge the quality of the muscle segmentation. In this case, the segmentation module 133 receives a quality indication from the user via the user interface 122 after the user has viewed the muscle segmentation.

[0031] If the quality of muscle segmentation is unacceptable, the segmentation module 133 can perform fine-tuning of the muscle segmentation. This can be done by region re-segmentation. Alternatively, if the quality of muscle segmentation is unacceptable, the user can manually edit the muscle segmentation using the user interface 122. For example, the user can perform fine-tuning by manually adjusting the boundaries of the target muscle region and / or small ROI in the longitudinal and / or transverse ultrasound images for the muscle of interest 155.

[0032] Figure 3A shows excellent segmentation results using a deep learning-based segmentation algorithm in a typical embodiment. Referring to Figure 3A, the target muscle region 301 is shown with a ground truth boundary 302 and a segmented (predicted) boundary 303 following muscle segmentation. The comparison between the ground truth boundary 302 and the segmented boundary 303 shows excellent alignment, where the Dice similarity coefficient is greater than 0.98. Therefore, only minor adjustments are required by the user to adjust the segmented boundary 303 of the target muscle region 301 to substantially match the ground truth boundary.

[0033] For comparison, Figure 3B shows relatively moderate to insufficient muscle segmentation results using a deep learning-based segmentation algorithm in a typical embodiment. Referring to Figure 3B, the target muscle region 311 is shown with a ground truth boundary 312 and a segmented (predicted) boundary 313 following muscle segmentation. The comparison between the ground truth boundary 312 and the segmented boundary 313 shows a fairly significant misalignment, where the Dice similarity coefficient is approximately 0.72. This misalignment may be due, for example, to the limited data size for the deep learning-based segmentation algorithm. Therefore, user editing is required to correct the mismatched boundary at the bottom of the target muscle region 311.

[0034] Referring again to Figure 2, the overall attenuation module 134 is configured to automatically calculate the overall attenuation (dB / cm) for each of the first and second target muscle regions 151 and 153 for longitudinal and transverse scans of the muscle of interest 155. The overall attenuation is determined within the boundaries of the first and second target muscle regions 151 and 153 using any known technique. For example, the first overall attenuation in the first target muscle region 151 from a longitudinal scan may be determined between the upper and lower boundaries, and the second overall attenuation in the second target muscle region 153 from a transverse scan may be determined between the left and right boundaries. The boundaries of the longitudinal and transverse scans are used for the averaged overall attenuation for each of the entire first and second target muscle regions 151 and 153 within a single frame of longitudinal and transverse ultrasound images. In general, the value of overall attenuation decreases as the amount of fat relative to muscle increases. In other words, the denser the tissue (i.e., the larger the proportion of muscle), the greater the overall attenuation of ultrasound waves passing through the tissue.

[0035] The local attenuation module 135 is configured to automatically calculate local attenuation coefficients (dB / cm / MHz) for small first and second regions of interest (ROIs) 152 and 154 within the first and second target muscle regions 151 and 153 for longitudinal and transverse scans, respectively, using any known technique. The local attenuation module 135 enables the selection of a first ROI 152 within the first target muscle region 151 from a single image frame, and a second ROI 154 within the second target muscle region 153 from a single image frame. The selection of the first and second ROIs 152 and 154 can be performed automatically by segmentation or manually by the user via the user interface 122. For example, each of the first and second ROIs 152 and 154 may be selected by the user via the user interface 122, or may be automatically set to a specific ROI in the central region of the target muscle regions 151 and 153, respectively. For example, the central regions of target muscle areas 151 and 153 can be automatically identified and cropped from their respective muscle segmentations to provide first and second ROIs 152 and 154. Once the first and second ROIs 152 and 154 are identified, a first local attenuation coefficient can be determined for the first ROI 152 from a longitudinal scan, and a second local attenuation coefficient can be determined for the second ROI 154 from a lateral scan. The local attenuation coefficients can be calculated directly for the first and second ROIs 152 and 154, or by averaging the corresponding attenuation maps.

[0036] Methods for determining the local attenuation coefficient for quantifying fatty liver may be adapted, for example, to determine the local attenuation coefficient of a targeted muscle region. For example, a linear ultrasound probe may be used to image a target muscle region instead of a curved ultrasound probe used to image the liver. Generally, the mean local attenuation coefficient for a normal liver is approximately 0.567 dB / cm / MHz, for mild fatty liver approximately 0.659 dB / cm / MHz, and for severe fatty liver approximately 0.789 dB / cm / MHz. The relative values ​​between the mean of normal, mild fatty, and severe fatty targeted muscle regions are similar.

[0037] The overall acoustic velocity module 136 is configured to automatically calculate the overall acoustic velocity (m / s) of ultrasound in the first and second target muscle regions 151 and 153, respectively, for longitudinal and transverse scans of the muscle of interest 155. The overall acoustic velocity value is determined within the boundaries of the first and second target muscle regions 151 and 153, respectively, by calculating the velocity of sound wave propagation through the tissue using any known technique. For example, the first overall acoustic velocity in the first target muscle region 151 from a longitudinal scan may be determined by averaging the acoustic velocity values ​​across the upper and lower boundaries, and the second overall acoustic velocity in the second target muscle region 153 from a transverse scan may be determined by averaging the acoustic velocity values ​​across the left and right boundaries. In general, the value of the overall acoustic velocity decreases as the amount of fat relative to the muscle increases. In other words, the acoustic speed of ultrasound passing through adipose tissue is approximately 1440 m / s, while the acoustic speed of ultrasound passing through muscle tissue is approximately 1585 m / s. Therefore, the denser the tissue (i.e., the greater the ratio of muscle to fat), the faster the ultrasound passes through it.

[0038] The local acoustic velocity module 137 is configured to automatically calculate the local acoustic velocity (m / s) of ultrasound in the first and second ROIs 152 and 154, respectively, within the first and second target muscle regions 151 and 153, for longitudinal and transverse scans of the muscle of interest, using any known technique. For example, the first local acoustic velocity may be determined for the first ROI 152, and the second local acoustic velocity may be determined for the second ROI 154. Each of the first and second ROIs 152 and 154 may be selected by the user or set to a specific ROI in the central region of the first and second target muscle regions 151 and 153, respectively. The local acoustic velocity may be calculated, for example, directly for the first and second ROIs 152 and 154, or by averaging their respective attenuation maps.

[0039] The sarcopenia grading module 138 is configured to determine the level (grade) of sarcopenia (e.g., normal sarcopenia, mild sarcopenia, moderate sarcopenia, severe sarcopenia) in the muscle of interest 155, thereby grading the sarcopenia. For example, normal sarcopenia (i.e., little or no sarcopenia) may receive a grade of 0 or A, mild sarcopenia may receive a grade of 1 or B, moderate sarcopenia may receive a grade of 2 or C, and severe sarcopenia may receive a grade of 3 or D. Different levels are defined by corresponding predetermined thresholds. The level of sarcopenia can be calculated by applying a regression model to a combination of eight parameters output by the global attenuation module 134, local attenuation module 135, global acoustic velocity module 136, and local acoustic velocity module 137, where four of the parameters are from longitudinal ultrasound images and four of the parameters are from transverse ultrasound images. The regression model determines weights for each of the eight parameters and, combined with the weighted parameters, outputs the level of sarcopenia based on a predetermined threshold.

[0040] In various embodiments, the regression model may be a machine learning algorithm such as an artificial neural network (ANN), a recurrent neural network (RNN), or a neural network model such as a CNN. Thus, the regression model may be trained using historically acquired data that provides the values ​​of eight parameters and the corresponding levels of sarcopenia in past patients. The training data may be stored in a database 112, for example. Training also compares the relative effects of the eight parameters on the resulting level of sarcopenia to establish the extent to which the eight parameters influence the final level of sarcopenia. Based on these relative effects, the regression model determines the respective weights and assigns them to the eight parameters. Current sarcopenia results and corresponding parameters can be added to this training database to continuously improve the accuracy of the regression model. That is, as will be apparent to those skilled in the art, training minimizes similarity loss to improve the accuracy of the regression model as the training dataset grows.

[0041] The weights applied by the regression model can explain a variety of different relationships between parameters, depending on their corresponding influence on the overall determination of sarcopenia. For example, global parameters from longitudinal and transverse ultrasound images (i.e., global attenuation and global acoustic velocity) may be weighted more heavily than local parameters from longitudinal and transverse ultrasound images (i.e., local attenuation coefficient and local acoustic velocity). Also, measured values ​​of attenuation (i.e., global attenuation and local attenuation coefficient) in longitudinal and transverse ultrasound images may be weighted slightly more heavily than measured values ​​of acoustic velocity (i.e., global and local acoustic velocity) in longitudinal and transverse ultrasound images.

[0042] In various embodiments, all or part of the process provided by the machine learning regression model may be implemented, for example, by the AI ​​engine described above. The neural network model may be trained using ground truth longitudinal and transverse ultrasound images of target muscle regions and ROIs in the muscles of interest of multiple patients. The longitudinal and transverse ultrasound images are associated with the corresponding global attenuation, local attenuation coefficients, global acoustic velocity, and local acoustic velocity determined for each, as well as the resulting sarcopenia grade. Thus, during the training phase, the neural network model learns an appropriate sarcopenia level from retrospective data applied to the current parameters.

[0043] The resulting sarcopenia level index is provided to the user, for example, via display 124, to diagnose whether sarcopenia is present in subject 150, and, if so, the level of sarcopenia (e.g., mild to severe) in subject 150. In one embodiment, the sarcopenia level may be provided to a reporting module (not shown) that formats information from a sarcopenia grading module 138 and displays the formatted information on display 124 via GUI 128. In addition to the sarcopenia level, the displayed information may include all or some of the parameters that the sarcopenia grading module 138 uses to determine the level of sarcopenia, including overall attenuation, local attenuation coefficients, overall acoustic velocity, and local acoustic velocity from longitudinal and transverse ultrasound images. In one embodiment, the longitudinal and transverse images themselves may also be displayed along with the sarcopenia grade and any additional information. Appropriate medical care may then be provided in consideration of the diagnosis.

[0044] Figure 4 is a flowchart of a method for evaluating sarcopenia in a subject using ultrasound imaging, according to a typical embodiment. The method may be implemented using instructions stored in memory 130 and executable by a processor 120 in system 100. In one embodiment, the method may be performed, for example, for evaluating muscle function in subjects at high risk of developing severe sarcopenia, by an online version to improve the MSK ultrasound workflow of an ultrasound imaging device.

[0045] Referring to Figure 4, in block S411, the longitudinal ultrasound image is received by a processor (e.g., processor 120), and the longitudinal ultrasound image is acquired by a longitudinal scan of the patient's muscle of interest using ultrasound imaging. The longitudinal scan is performed after the ultrasound imaging device has been pre-configured for MSK imaging. The longitudinal ultrasound image may be received in real time or near real time from the ultrasound imaging device (e.g., ultrasound imaging device 140) during the ultrasound imaging session, or it may be retrieved from a database (e.g., database 112) that stores images acquired during the previous ultrasound imaging session.

[0046] In block S412, a lateral ultrasound image is received by the processor, which is acquired by a lateral scan of the patient's muscle of interest using ultrasound imaging. The lateral ultrasound image may be received in real time or near real time from the ultrasound imaging device during the ultrasound imaging session, or it may be retrieved from a database.

[0047] In block S413, longitudinal and transverse ultrasound images are segmented to identify target muscle regions of the subject's muscles. Specifically, the longitudinal ultrasound image is segmented to identify a first target muscle region, and the transverse ultrasound image is segmented to identify a second target muscle region.

[0048] In block S414, the overall attenuation in the target muscle region is determined between the boundaries of the target muscle region in the longitudinal and transverse ultrasound images, respectively. That is, the first overall attenuation in the first target muscle region is determined between the upper and lower boundaries defining the first target muscle region, and the second overall attenuation in the second target muscle region is determined between the left and right boundaries defining the second target muscle region.

[0049] In block S415, regions of interest (ROIs) are identified in the target muscle region in the longitudinal and transverse ultrasound images, respectively. That is, a first ROI is identified in the first target muscle region, and a second ROI is identified in the second target muscle region. Each of the first and second ROIs may be identified manually by the user or automatically by the processor. For example, the first ROI may be identified in the central region of the first target muscle region, and the second ROI may be identified in the central region of the second target muscle region, where the central region may be automatically identified and cut out from the segmented target muscle region.

[0050] In block S416, the local attenuation coefficients of the ROIs are determined for each target muscle region. Specifically, the first local attenuation coefficient is determined for the first ROI in the first target muscle region, and the second local attenuation coefficient is determined for the second ROI in the second target muscle region.

[0051] In block S417, the overall acoustic velocity within the target muscle region is determined between the boundaries of the target muscle region in the longitudinal and transverse ultrasound images, respectively. Specifically, the first overall acoustic velocity in the first targeted muscle region is determined between the upper and lower boundaries defining the first targeted muscle region, and the second overall acoustic velocity in the second targeted muscle region is determined between the left and right boundaries defining the second targeted muscle region.

[0052] In block S418, the local acoustic velocity within the ROI in the target muscle region is determined in the longitudinal and transverse ultrasound images, respectively. That is, the first local acoustic velocity in the first ROI is determined in the first target muscle region, and the second local acoustic velocity in the second ROI is determined in the second target muscle region.

[0053] In block S419, the level of sarcopenia in the subject is determined (graded) based on the overall attenuation and overall acoustic velocity in the first and second target muscle regions, and the local attenuation coefficients and local acoustic velocity through the first and second ROIs. The level of sarcopenia can be determined, as described above, by applying a regression model to the overall attenuation and overall acoustic velocity of the first and second target muscle regions, and to the local attenuation coefficients and local acoustic velocity of the first and second ROIs, respectively.

[0054] In block S420, the level of sarcopenia is reported to the user via a display or GUI as a result of the patient's diagnosis. The user can then determine an appropriate course of medical treatment. Reporting may further include displaying representative image frames for each of the longitudinal and transverse scans. Additionally, one or more of the first overall attenuation, local attenuation coefficient, overall acoustic velocity, and local acoustic velocity may be displayed with the image frame for the longitudinal scan, and one or more of the second overall attenuation, local attenuation coefficient, overall acoustic velocity, and local acoustic velocity may be displayed with the image frame for the transverse scan.

[0055] According to various embodiments of this disclosure, the methods described herein may be implemented using a hardware computer system that executes software programs stored in a non-temporary storage medium. Furthermore, in exemplary, non-limiting embodiments, implementations may include distributed processing, component / object distributed processing, and parallel processing. Virtual computer system processing may implement one or more of the methods or functions described herein, and the processors described herein may be used to support a virtual processing environment.

[0056] While the assessment of sarcopenia using ultrasound imaging has been described with reference to exemplary embodiments, it should be understood that the language used is descriptive and illustrative, not limiting. Modifications can be made within the scope of the appended claims, as now described and modified, without departing from the scope and spirit of the embodiments. While the assessment of sarcopenia using ultrasound imaging has been described with reference to specific means, materials, and embodiments, it is not intended to be limited to the disclosed details, but rather to encompass all functionally equivalent structures, methods, and uses as those described in the appended claims.

[0057] The descriptions of embodiments described herein are intended to provide a general understanding of the structure of various embodiments. The illustrations are not intended to serve as a complete description of all elements and features of the disclosure described herein. Many other embodiments may be apparent to those skilled in the art upon consideration of the disclosure. Other embodiments may be utilized and derived from the disclosure so as to be structural and logical substitutions and modifications without departing from the scope of the disclosure. In addition, the descriptions are merely illustrative and may not be drawn to scale. Certain proportions in the figures are exaggerated, while other proportions are minimized. Therefore, the disclosure and drawings should be considered illustrative rather than restrictive.

[0058] One or more embodiments of this disclosure may be referred to individually and / or collectively by the term “invention” in this specification, merely for convenience and without the intention of voluntarily limiting the scope of this application to any particular invention or inventive concept. Furthermore, while certain similarities have been illustrated and described herein, it should be understood that any subsequent configuration designed to achieve the same or similar objectives may be substituted for the specific similarities shown. This disclosure is intended to cover any and all subsequent adaptations or variations of the various embodiments. Combinations of the embodiments described above, and other embodiments not specifically described herein, will become apparent to those skilled in the art by examining the description.

[0059] This abstract of the disclosure is provided in accordance with 37 CFR §1.72(b) and is submitted under the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Furthermore, in the preceding detailed description, various features may be grouped together or described in a single embodiment for the purpose of simplifying the disclosure. The disclosure should not be construed as reflecting an intention that the claimed embodiments require more features than are expressly enumerated in each claim. Rather, as the following claims reflect, the subject matter of the invention may cover fewer features than all of any of the disclosed embodiments. Accordingly, the following claims are incorporated into the detailed description, and each claim stands independently as defining the subject matter that is claimed separately.

[0060] The foregoing description of the disclosed embodiments is provided to enable those skilled in the art to implement the concepts described herein. Accordingly, the subject matter disclosed above should be considered illustrative and not restrictive, and the appended claims are intended to encompass all such modifications, enhancements, and other embodiments that fall within the true spirit and scope of this disclosure. Therefore, to the maximum extent permitted by law, the scope of this disclosure should be determined by the broadest and most acceptable interpretation of the following claims and their equivalents, and not limited or restricted by the foregoing detailed description.

Claims

1. A method for operating a system for diagnosing sarcopenia in a patient, wherein the system has a processor, and the method is The processor receives a longitudinal ultrasound image obtained by a longitudinal scan of the muscle of interest in the patient using ultrasound imaging. The processor receives a lateral ultrasound image obtained by a lateral scan of the muscle of interest in the patient using ultrasound imaging. The processor segments the longitudinal and transverse ultrasound images, respectively, to identify the first and second target muscle regions of the muscle of interest in the longitudinal and transverse ultrasound images, respectively. The processor performs the steps of determining the overall attenuation in the first target muscle region between an upper boundary and a lower boundary defining the first target muscle region in the longitudinal ultrasound image, The processor determines the overall attenuation in the second target muscle region between the left boundary and the right boundary defining the second target muscle region in the lateral ultrasound image, The processor includes the steps of identifying regions of interest in the first and second target muscle regions in the longitudinal ultrasound image and the transverse ultrasound image, respectively. The processor performs the steps of determining local attenuation coefficients in the regions of interest in the first and second target muscle regions, respectively. The processor performs the steps of determining the overall acoustic velocity in the first target muscle region between an upper boundary and a lower boundary defining the first target muscle region in the longitudinal ultrasound image, The processor performs the steps of determining the overall acoustic velocity in the second target muscle region between the left boundary and the right boundary defining the second target muscle region in the lateral ultrasound image, The processor performs the steps of determining the local acoustic velocity in the region of interest in the first and second target muscle regions, respectively. The processor determines the level of sarcopenia in the patient based on the overall attenuation and overall acoustic velocity in the first and second target muscle regions, and the local attenuation coefficient and local acoustic velocity in the region of interest in the first and second target muscle regions. A method having.

2. The method according to claim 1, wherein the step of the processor segmenting the longitudinal and transverse ultrasound images comprises the step of the processor applying a deep learning-based segmentation algorithm to the image data from the longitudinal and transverse images.

3. The method according to claim 2, wherein the step of the processor segmenting the longitudinal and transverse ultrasound images further comprises the step of the processor fine-tuning or editing at least one of the first and second target muscle regions to match the ground truth boundary.

4. The method according to claim 1, wherein the step of the processor determining the level of sarcopenia in the patient comprises the step of the processor applying a regression model to the overall attenuation and overall acoustic velocity in the first and second target muscle regions, and to the local attenuation coefficient and local acoustic velocity in the region of interest in the first and second target muscle regions.

5. The step of the processor identifying regions of interest in the first and second target muscle regions is: The processor receives from the user via a user interface a selection of local attenuation coefficients in the regions of interest in the first and second target muscle regions, or The processor automatically selects regions of interest in the first and second target muscle regions within the central region of the first and second target muscle regions. The method according to claim 1, having the following characteristics.

6. The method of claim 5, wherein the step of the processor determining local attenuation coefficients in the regions of interest in the first and second target muscle regions comprises the step of the processor averaging the attenuation maps from the regions of interest in the first and second target muscle regions, respectively.

7. The method according to claim 1, wherein the step of the processor determining the overall acoustic velocity through the first and second target muscle regions comprises the step of the processor averaging the acoustic velocities through the boundaries of the first and second target muscle regions, respectively.

8. The method according to claim 7, wherein the step of the processor determining local acoustic velocities through regions of interest in the first and second target muscle regions comprises the step of the processor averaging acoustic velocity maps from the regions of interest in the first and second target muscle regions, respectively.

9. The system further comprises a display, The steps include: the display showing a representative image frame for each of the vertical scan and the horizontal scan; The steps include: displaying on the display one or more of the following: the overall attenuation in the first target muscle region, the local attenuation coefficient in the region of interest in the first target muscle region, the overall acoustic velocity in the first target muscle region, and the local acoustic velocity with respect to the longitudinal ultrasound image, together with a representative image frame for the longitudinal scan; and displaying on the display one or more of the following: the overall attenuation in the second target muscle region, the local attenuation coefficient in the region of interest in the second target muscle region, the overall acoustic velocity in the second target muscle region, and the local acoustic velocity with respect to the transverse ultrasound image, together with a representative image frame for the transverse scan. The method according to claim 1, further comprising:

10. Steps to report diagnostic results corresponding to the level of sarcopenia The method according to claim 1, further comprising:

11. A system for diagnosing sarcopenia in patients, wherein the system is User interface and A processor that communicates with the aforementioned user interface, Non-temporary memory, which, when executed by the processor, the processor, The steps include receiving a longitudinal ultrasound image obtained by longitudinal scanning of the muscle of interest in the patient using ultrasound imaging, The steps include receiving a lateral ultrasound image obtained by a lateral scan of the muscle of interest in the patient using ultrasound imaging, The steps include segmenting the longitudinal and transverse ultrasound images to identify the first and second target muscle regions of the muscle of interest in the longitudinal and transverse ultrasound images, respectively. The steps include determining the overall attenuation in the first target muscle region between the upper boundary and the lower boundary defining the first target muscle region in the longitudinal ultrasound image, The steps include determining the overall attenuation in the second target muscle region between the left boundary and the right boundary defining the second target muscle region in the lateral ultrasound image, The steps include identifying regions of interest in the first and second target muscle regions in the longitudinal and transverse ultrasound images, respectively, The steps include determining the local attenuation coefficients in the regions of interest in the first and second target muscle regions, respectively, The steps include determining the overall acoustic velocity in the first target muscle region between the upper boundary and the lower boundary defining the first target muscle region in the longitudinal ultrasound image, The steps include determining the overall acoustic velocity in the second target muscle region between the left boundary and the right boundary defining the second target muscle region in the lateral ultrasound image, The steps include determining the local acoustic velocity in the region of interest in the first and second target muscle regions, respectively, A step of determining the level of sarcopenia in the patient based on the overall attenuation and overall acoustic velocity in the first and second target muscle regions, and the local attenuation coefficient and local acoustic velocity in the region of interest in the first and second target muscle regions. Non-temporary memory that stores the instructions to execute, A display configured to show an indicator of the level of sarcopenia and A system that has

12. The system according to claim 11, wherein the instruction causes the processor to perform the step of segmenting the longitudinal ultrasound image and the transverse ultrasound image by applying a deep learning-based segmentation algorithm to the image data from the longitudinal ultrasound image and the transverse ultrasound image.

13. The system according to claim 12, wherein the step of segmenting the longitudinal and transverse ultrasound images further comprises the step of fine-tuning or editing at least one segmented boundary of the first and second target muscle regions to coincide with the ground truth boundary.

14. The system according to claim 11, wherein the instruction causes the processor to perform the step of determining the level of sarcopenia in the patient by applying a regression model to the overall attenuation and overall acoustic velocity in the first and second target muscle regions and to the local attenuation coefficient and local acoustic velocity in the region of interest in the first and second target muscle regions.

15. The aforementioned instruction is given to the processor, The user interface receives a selection from the user for the local attenuation coefficient in the region of interest in the first and second target muscle regions, or Steps to automatically select regions of interest in the first and second target muscle regions in the central region of the first and second target muscle regions. The system according to claim 11, wherein the system performs the step of identifying regions of interest of the first and second target muscle regions.

16. The system according to claim 15, wherein the instruction causes the processor to perform the step of determining a local attenuation coefficient in the region of interest in the first and second target muscle regions by averaging the attenuation maps from the region of interest in the first and second target muscle regions, respectively.

17. The system according to claim 11, wherein the instruction causes the processor to perform the step of determining the overall acoustic velocity through the first and second target muscle regions by averaging the acoustic velocities through the boundaries of the first and second target muscle regions, respectively.

18. The system according to claim 17, wherein the step of determining the local acoustic velocity through the regions of interest in the first and second target muscle regions comprises the step of averaging the acoustic velocity maps from the regions of interest in the first and second target muscle regions, respectively.

19. The aforementioned display further, The steps include displaying a representative image frame for each of the aforementioned vertical scans and horizontal scans, A step of displaying one or more of the following, together with a representative image frame for the longitudinal scan: the overall attenuation in the first target muscle region, the local attenuation coefficient in the region of interest in the first target muscle region, the overall acoustic velocity in the first target muscle region, and the local acoustic velocity with respect to the longitudinal ultrasound image; A step of displaying one or more of the following, together with a representative image frame for the lateral scan: the overall attenuation in the second target muscle region, the local attenuation coefficient in the region of interest in the second target muscle region, the overall acoustic velocity in the second target muscle region, and the local acoustic velocity with respect to the lateral ultrasound image. The system according to claim 11, configured to perform the task.

20. A non-temporary computer-readable medium for storing instructions for diagnosing sarcopenia in a patient, which, when executed by a processor, the processor, The steps include receiving a longitudinal ultrasound image obtained by longitudinal scanning of the muscle of interest in the patient using ultrasound imaging, The steps include receiving a lateral ultrasound image obtained by a lateral scan of the muscle of interest in the patient using ultrasound imaging, The steps include segmenting the longitudinal and transverse ultrasound images to identify the first and second target muscle regions of the muscle of interest in the longitudinal and transverse ultrasound images, respectively. The steps include determining the overall attenuation in the first target muscle region between the upper boundary and the lower boundary defining the first target muscle region in the longitudinal ultrasound image, The steps include determining the overall attenuation in the second target muscle region between the left boundary and the right boundary defining the second target muscle region in the lateral ultrasound image, The steps include identifying regions of interest in the first and second target muscle regions in the longitudinal and transverse ultrasound images, respectively, The steps include determining the local attenuation coefficients in the regions of interest in the first and second target muscle regions, respectively, The steps include determining the overall acoustic velocity in the first target muscle region between the upper boundary and the lower boundary defining the first target muscle region in the longitudinal ultrasound image, The steps include determining the overall acoustic velocity in the second target muscle region between the left boundary and the right boundary defining the second target muscle region in the lateral ultrasound image, The steps include determining the local acoustic velocity in the region of interest in the first and second target muscle regions, respectively, A step of determining the level of sarcopenia in the patient based on the overall attenuation and overall acoustic velocity in the first and second target muscle regions, and the local attenuation coefficient and local acoustic velocity in the region of interest in the first and second target muscle regions, The steps include displaying an indicator of the level of sarcopenia and A non-temporary computer-readable medium configured to execute [something].

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