(144 days)
Not Found
Yes
The document explicitly states that the device uses a "machine learning-based approach" and "artificial intelligence (AI)" for segmentation, and mentions training "machine learning algorithms, specifically convolutional networks (CNNs)".
No
The device is described as a software-only product that provides segmentation and derived metrics from MRI images; it explicitly states "cannot serve as direct guidance for diagnosis of any diseases" and does not claim to treat or diagnose conditions.
No.
The device is explicitly stated to "cannot serve as direct guidance for diagnosis of any diseases." It provides segmentation and derived metrics which need to be reviewed and edited by physicians trained to interpret MRI images, acting as an initial method rather than a diagnostic tool.
Yes
The device description explicitly states "MuscleView is a software only product". While it processes medical images and requires a workstation with a GPU, these are the environment and input data, not hardware components included as part of the device itself.
Based on the provided text, MuscleView is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens derived from the human body (like blood, urine, tissue) to provide information for diagnosis, monitoring, or screening.
- MuscleView's Function: MuscleView analyzes medical images (MRI scans) of the human body. It does not process or analyze biological specimens.
- Intended Use: The intended use clearly states that it segments muscle and bone structures from MRI images and provides derived metrics. It explicitly states it "cannot serve as direct guidance for diagnosis of any diseases."
Therefore, MuscleView falls under the category of medical image analysis software, not In Vitro Diagnostics.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
MuscleView is used in adults and pediatics aged 18 and older to automatically segment muscle and bone structures of the lower extremities from magnetic resonance imaging using a machine learning-based approach. After segmentation, it can provide derived metrics including muscle volume, intramuscular fat percentage, and left/right asymmetry.
It is intended to be used by physicians who are trained to interpret MRI images, and serves as an initial method to segment muscle and bone structures from one or more study series. The segmentation results need to be reviewed and edited using appropriate software.
It is intended to only provide the segmentation and derived metrics for muscle and bone structures and cannot serve as direct guidance for dagnosis of any diseases. This device is not intents who have tumors in lower limb.
Product codes
LNH
Device Description
MuscleView is a software only product that uses a machine learning-based approach for the automatic segmentation of musculoskeletal structures from MRI. Based on the segmentation, metrics such as volume and length of the segmented structures are calculated.
The software has the following modules: user management, data management, image processing, Al segmentation & 3D model viewer and metrics calculation. User management involves authentication and access to the software and its results. Data management involves medical image data and its interactions with the system workflow. Image processing involves Preprocessing the DICOM data to create a combined continuous 3D volume(s) of series with similar settings for use in Al segmentation & 3D model viewer module handles training data and algorithms to obtain the pre-trained models and algorithms to update models. Metric calculation module handles the final calculation of relevant metrics.
Input data is preprocessed and prepared for 3D volume segmentation of the musculoskeletal structures. A library of already contoured expert cases is utilized to train the machine learning algorithms, specifically convolutional networks (CNNs) perform automated segmentation. This process is in an auxiliary module for AI training.
MuscleView is intended to be used by physicians who are trained to interpret MRI images, and serves as an initial method to segment muscle and bone structures from one or more study series. The segmentation results need to be reviewed and edited using appropriate software. This device is not intended for use with patients who have tumors in lower limb. The currently supported anatomical regions for automatic segmentation are 80 different muscles and bones of the lower extremity.
Upon segmentation, a suite of metrics regarding the segmented 3D volumes is provided. It is intended to only provide the segmentation and derived metrics for muscle and bone structures and cannot serve as direct guidance for diagnosis of any diseases. These metrics include segmentation volume, fat infiltration (if applicable), and limb side asymmetry. The metrics are provided in conjunction with an interactive visualization of the 3D segmentation results.
The software is deployed within a private network on a workstation with an advanced graphic processing unit (GPU) and runs as a service. A web-based interface is used to access the service and manage the data transfer, automatic segmentation, and visualization.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
MRI
Anatomical Site
lower extremities, lower limb
Indicated Patient Age Range
adults and pediatics aged 18 and older
Intended User / Care Setting
physicians who are trained to interpret MRI images
Description of the training set, sample size, data source, and annotation protocol
The artificial intelligence (AI) responsible for muscle segmentation within MuscleView was trained on MRI scans generated across varying patients, demographics (including – but not limited to - gender, age and ethnicity), scan sites, and MRI parameters (including manufacturer, magnetic field strength, series settings/types, matrix size, field of view, and scan resolution).
Number of unique scans: 1658
Number of unique subjects: 1294
Gender: Male (1192*), Female (374*) (*Demographic data were not available for some scans.)
Age: Mean 29, Standard Deviation 13.4
Ethnicity (based on regional demographics): % Non-Hispanic White (52), % Hispanic/Latino (18), % Black/African American (14), % Asian (10), % Australian (2), % American Indian / Alaska Native (21 years old, and females >21 years old) also demonstrated that each subgroup passed the predetermined acceptance criteria.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice similarity coefficient (DSC), Volume difference (VDt)
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.1000 Magnetic resonance diagnostic device.
(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: a symbol on the left and the FDA name on the right. The symbol on the left is a stylized image of a human figure, while the FDA name on the right is written in blue letters. The words "U.S. FOOD & DRUG ADMINISTRATION" are written in a clear, sans-serif font.
October 1, 2024
Springbok, Inc. Scott Magargee Chief Executive Officer 110 Old Preston Ave Charlottesville, Virginia 22902
Re: K241331
Trade/Device Name: MuscleView Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic Resonance Diagnostic Device Regulatory Class: Class II Product Code: LNH Dated: August 19, 2024 Received: August 19, 2024
Dear Scott Magargee:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
1
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review. the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
2
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
D. G. K.
Daniel M. Krainak, Ph.D. Assistant Director DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
3
Indications for Use
510(k) Number (if known) K241331
Device Name MuscleView
Indications for Use (Describe)
MuscleView is used in adults and pediatics aged 18 and older to automatically segment muscle and bone structures of the lower extremities from magnetic resonance imaging using a machine learning-based approach. After segmentation, it can provide derived metrics including muscle volume, intramuscular fat percentage, and left/right asymmetry.
It is intended to be used by physicians who are trained to interpret MRI images, and serves as an initial method to segment muscle and bone structures from one or more study series. The segmentation results need to be reviewed and edited using appropriate software.
It is intended to only provide the segmentation and derived metrics for muscle and bone structures and cannot serve as direct guidance for dagnosis of any diseases. This device is not intents who have tumors in lower limb.
| Type of Use (Select one or both, as applicable) |
|---|
| ------------------------------------------------- |
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
4
Device Name: MuscleView
Date Summary was Prepared: September 30th, 2024
1. Applicant:
Springbok, Inc.
110 Old Preston Ave
Charlottesville, VA 22902 USA
Contact Name: Scott Magargee – Chief Executive Officer
Phone: 1-215-680-9078
Fax: N/A
E-mail: scott.magargee@springbokanalytics.com
2. Device:
Trade Name: MuscleView Common Name: MuscleView Model Number: v1.0 Product Code: LNH Regulation Description: Magnetic Resonance Diagnostic Device Regulation Number: 21 CFR 892.1000 Device Class: II
3. Predicate Devices:
Trade Name: AMRA Profiler
Manufacturer: AMRA Medical AB
5
Address: 68 Southwood Ter SOUTHBURY, CT 06488 Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic resonance diagnostic device Device Class: Class II Product Code: LNH 510(k) Number: K173749 510(k) Clearance Date: Dec 06, 2018
4. Device Description
MuscleView is a software only product that uses a machine learning-based approach for the automatic segmentation of musculoskeletal structures from MRI. Based on the segmentation, metrics such as volume and length of the segmented structures are calculated.
The software has the following modules: user management, data management, image processing, Al segmentation & 3D model viewer and metrics calculation. User management involves authentication and access to the software and its results. Data management involves medical image data and its interactions with the system workflow. Image processing involves Preprocessing the DICOM data to create a combined continuous 3D volume(s) of series with similar settings for use in Al segmentation & 3D model viewer module handles training data and algorithms to obtain the pre-trained models and algorithms to update models. Metric calculation module handles the final calculation of relevant metrics.
Input data is preprocessed and prepared for 3D volume segmentation of the musculoskeletal structures. A library of already contoured expert cases is utilized to train the machine learning algorithms, specifically convolutional networks (CNNs) perform automated segmentation. This process is in an auxiliary module for AI training.
MuscleView is intended to be used by physicians who are trained to interpret MRI images, and serves as an initial method to segment muscle and bone structures from one or more study series. The segmentation results need to be reviewed and edited using appropriate software. This device is not intended for use with patients who have tumors in lower limb. The currently supported anatomical regions for automatic segmentation are 80 different muscles and bones of the lower extremity. The supported musculoskeletal structures for each region are shown below in Table 1.
6
Upon segmentation, a suite of metrics regarding the segmented 3D volumes is provided. It is intended to only provide the segmentation and derived metrics for muscle and bone structures and cannot serve as direct guidance for diagnosis of any diseases. These metrics include segmentation volume, fat infiltration (if applicable), and limb side asymmetry. The metrics are provided in conjunction with an interactive visualization of the 3D segmentation results.
The software is deployed within a private network on a workstation with an advanced graphic processing unit (GPU) and runs as a service. A web-based interface is used to access the service and manage the data transfer, automatic segmentation, and visualization.
Table 1: List of supported musculoskeletal structures for segmentation. Each ROI is supported for both left and right sides, which are analyzed and validated independently. *Gemelli is a grouping of the superior and inferior gemellus. **Fibulari is a grouping of the fibularis brevis and fibularis longus. ***Phalangeal extensors are a grouping of the extensor digitorum longus, extensor hallucis longus, and fibularis tertius.
| Structure Name | Structure Type |
|---|---|
| Adductor brevis | Muscle |
| Adductor longus | Muscle |
| Adductor magnus | Muscle |
| Biceps femoris (long head) | Muscle |
| Biceps femoris (short head) | Muscle |
| Fibulari** | Muscle |
| Flexor Digitorum Longus | Muscle |
| Flexor Hallucis Longus | Muscle |
| Gastrocnemius (lateral head) | Muscle |
| Gastrocnemius (medial | |
| head) | Muscle |
| Gemelli* | Muscle |
| Gluteus maximus | Muscle |
| Gluteus medius | Muscle |
| Gluteus minimus | Muscle |
| Gracilis | Muscle |
| Iliacus | Muscle |
| Obturator externus | Muscle |
| Obturator internus | Muscle |
| Pectineus | Muscle |
| Phalangeal extensors*** | Muscle |
| Piriformis | Muscle |
| Popliteus | Muscle |
| Psoas major | Muscle |
| Quadratus femoris | Muscle |
| Quadratus lumborum | Muscle |
| Rectus femoris | Muscle |
| Sartorius | Muscle |
| Semimembranosus | Muscle |
| Semitendinosus | Muscle |
| Soleus | Muscle |
| Tensor fasciae latae | Muscle |
| Tibialis anterior | Muscle |
| Tibialis Posterior | Muscle |
| Vastus intermedius | Muscle |
| Vastus lateralis | Muscle |
| Vastus medialis | Muscle |
| Pelvis | Bone |
| Femur | Bone |
| Tibia | Bone |
| Fibula | Bone |
7
5. Indications for Use Statement
MuscleView is used in adults and pediatric patients aged 18 and older to automatically segment muscle and bone structures of the lower extremities from magnetic resonance imaging using a
8
machine learning-based approach. After segmentation, it can provide derived metrics including muscle volume, bone volume, intramuscular fat percentage, and left/right asymmetry.
lt is intended to be used by physicians who are trained to interpret MRI images, and serves as an initial method to segment muscle and bone structures from one or more study series. The segmentation results need to be reviewed and edited using appropriate software.
It is intended to only provide the segmentation and derived metrics for muscle and bone structures and cannot serve as direct guidance for diagnosis of any diseases. This device is not intended for use with patients who have tumors in lower limb.
6. Summary of Technoloqical Characteristics Comparison
The similarities and differences between the technological characteristics of the two products are shown in Table 2. The key difference is the detailed implementation of the automated segmentation algorithms. Testing demonstrates that the differences do not raise new questions of safety or effectiveness.
| Topic | AMRA Profiler (510k Number:
K173749) | MuscleView |
|------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|
| Physical
Characteristics | A service that is provided with a
cloud-based service using an
automated image-analysis pipeline
(with manual quality control for scan
preprocessing and label quality
control) | Software package that operates on a
virtual machine within off-the-shelf
hardware |
| Computer | Not applicable | PC Compatible |
| DICOM
Standard
Compliance | The service processes DICOM
compliant image data in accordance
with a required MRI protocol. | The software processes DICOM
compliant image data |
| Modalities | MRI | MRI |
| MRI
Parameters of
Importance | Collected Series: 3D Dixon water and
fat phase.
Region of Interest: Lower extremity,
Upper body as well
Supported Field Strength: | The same except that we support a
larger variability in scan properties
and coverage |
Table 2. Summary of technological characteristic comparison.
9
| | Direction of Capture: Axial
Supported In-Plane Resolution:
Supported Slice Spacing:
Note: Rigid conformity to MRI protocol is needed. | |
|---------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| User Interface | Concierge Service. Data is provided and analyzed, and results returned | The software is designed for use on a workstation with a web-based user interface. Functionalities are largely the same. |
| Segmentation Structures | 12 individual muscles and 6 muscle groups across the lower and upper extremity and liver | Eighty (8 bones, 72 muscles) individual structures on both the left and right side for the lower extremity focused region. |
| Segmentation Metrics | Muscle Volume, Fat Fraction | Structure volume, muscle fat infiltration, and derived metrics including asymmetry, muscle length, and cross-sectional area |
| Overall Segmentation Method | Labeled muscle segmentations automatically generated using non-rigid image registration to atlases utilizing 2D analysis techniques | AI segmentation model-based approach using a library of expert contours for training, MuscleView uses a machine learning-based method to train CNNs from expert contours to perform segmentation on the target images to generate contours |
| Support of Manual Editing by customer | No | No |
7. Performance Data
The safety and performance of MuscleView have been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Non-clinical verification and validation test results, including model performance and software usability, established that the device meets its design requirements and intended use, that it is as safe and as effective as the AMRA Profiler (510k Number: K173749), and that no new issues of safety and effectiveness were raised.
10
Further, during development of the software, potential hazards were controlled by a risk management plan including risk analysis, risk mitigation, and validation.
8. Summary of Al Validation
The artificial intelligence (AI) responsible for muscle segmentation within MuscleView was trained and tested on MRI scans generated across varying patients, demographics (including – but not limited to - gender, age and ethnicity), scan sites, and MRI parameters (including manufacturer, magnetic field strength, series settings/types, matrix size, field of view, and scan resolution). See Table 3 below detailing variation in the data.
Table 3. Summary of Al training and validation dataset's demographics. *Demographic data were not available for some scans.
| Factor | Groups | Training data | Validation data |
|---|---|---|---|
| Number of unique scans | n/a | 1658 | 148 |
| Number of unique subjects | n/a | 1294 | 148 |
| Gender | Male (%) | 1192* | 102 |
| Female (%) | 374* | 46 | |
| Age* | Mean | 29 | 31.7 |
| Standard Deviation | 13.4 | 15.5 | |
| Ethnicity | |||
| (based on regional | |||
| demographics) | % Non-Hispanic White | 52 | 52 |
| % Hispanic/Latino | 18 | 18 | |
| % Black/African American | 14 | 14 | |
| % Asian | 10 | 10 | |
| % Australian | 2 | 2 | |
| % American Indian / | |||
| Alaska Native | 21 years old, and females >21 years old) also demonstrated that each subgroup passed the predetermined |
12
acceptance criteria. For the 95% confidence interval for all structures and subgroups, see Table 4 (DSC) and Table 5 (VDt).
Table 4: Dice similarity coefficient 95% confidence interval for all 80 musculoskeletal structures across all subgroup analyses.
| Descriptive Statistics (DSC) 95% Confidence Interval | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| ROI | Pathology Level | ||||||||||
| Subgroup Analysis | MRI Manufacturer | ||||||||||
| Subgroup Analysis | Age/Biologic Sex | ||||||||||
| Subgroup Analysis | |||||||||||
| Healthy | Patient | Canon | GE | Philips | Siemens | Toshiba | Years | Years | years | Male > 21 Male 18-21 Female > 21 Female 18-21 | |
| years | |||||||||||
| adductor brevis | 0.962-0.965 0.909-0.932 0.978-0.984 0.947-0.967 0.937-0.954 0.956-0.96 0.956-0.96 0.95-0.959 | 0.958-0.963 | 0.911 -0.941 | ||||||||
| adductor longus | 0.978-0.979 0.934-0.952 0.982-0.989 0.965-0.98 0.954-0.972 0.962-0.9710.971-0.975 0.97-0.982 | 0.968 -0.975 | 0.974 -0.978 | 0.936 -0.96 | |||||||
| adductor magnus | 0.988-0.989 0.963-0.974 0.994-0.997 0.981-0.992 0.976-0.986 0.979-0.9840.981-0.9850.984-0.991 0.982-0.987 | 0.987 -0.99 | 0.968 -0.978 | ||||||||
| biceps femoris: long head | 0.981 -0.982 0.946 -0.964 0.976 -0.985 0.964 -0.977 0.967 -0.976 0.975 -0.98 0.972 -0.983 -0.97 -0.978 | 0.979-0.982 | 0.956 -0.974 | ||||||||
| biceps femoris: short head | 0.96-0.969 0.88-0.931 0.827-1.025 0.919-0.979 0.93-0.967 0.936-0.9550.951-0.9570.967-0.972 0.943-0.961 | 0.953-0.96 | 0.887 -0.94 | ||||||||
| external rotators | 0.867-0.876 0.809-0.834 0.884-0.909 0.821-0.8510.846-0.867 0.852-0.8670.869-0.8830.868-0.881 0.861-0.874 | 0.843 -0.861 | 0.809 -0.837 | ||||||||
| femur | 0.987-0.99 0.979-0.984 0.921-0.985 0.983-0.9910.986-0.9880.988-0.989 0.99-0.992 0.984-0.988 | 0.989 -0.991 | 0.981 -0.985 | ||||||||
| fibula | 0.914-0.927 0.797-0.852 0.941-0.958 0.904-0.9190.909-0.924 0.858-0.895 0.856-0.94 0.918-0.931 0.902-0.92 | 0.867 -0.919 | 0.773 -0.86 | ||||||||
| fibulari | 0.961-0.969 0.918-0.946 0.978-0.983 0.962-0.97 0.956-0.968 0.942-0.96 0.922-0.9720.963-0.972 0.957-0.966 | 0.939-0.97 | 0.91 -0.954 | ||||||||
| flexor digitorum longus | 0.863-0.882 0.827-0.851 0.905-0.935 0.855-0.8740.853-0.875 0.875 0.875 0.875 0.875 -0.898 0.869-0.882 | 0.793 -0.864 | 0.822 -0.857 | ||||||||
| flexor hallucis longus | 0.94-0.952 0.869-0.909 0.963-0.976 0.94-0.953 0.932-0.942 0.91-0.936 0.879-0.9560.949-0.956 0.926-0.942 | 0.907 -0.956 | 0.871 -0.927 | ||||||||
| Gastrocnemius: lateral head 0.972 -0.974 0.982-0.986 0.969-0.976 0.964 0.966-0.97 0.97-0.976 0.953-0.969 | 0.97 -0.974 | 0.938 -0.962 | |||||||||
| Gastrocnemius: medial head 0.979-0.983 0.928-0.993 0.979-0.985 0.969-0.98 0.951-0.9690.971-0.9810.972-0.985 0.96 0.976 | 0.977 -0.984 | 0.949 -0.969 | |||||||||
| gluteus maximus | 0.993-0.994 0.977-0.985 0.998-0.998 0.99-0.995 0.99-0.993 0.993-0.9940.991-0.995 0.988-0.993 | 0.994 -0.995 | 0.983 -0.988 | ||||||||
| gluteus medius | 0.985-0.987 0.966-0.975 0.993-0.995 0.981-0.9890.977-0.983 0.977-0.9820.983-0.988 0.98 -0.98 -0.98 -0.984 | 0.985 -0.988 | 0.97 -0.979 | ||||||||
| gluteus minimus | 0.964-0.966 0.929-0.946 0.974-0.982 0.949-0.9660.951-0.959 0.951-0.96 0.957-0.9630.965-0.969 0.954-0.963 | 0.959 -0.965 | 0.937 -0.95 | ||||||||
| gracilis | 0.969-0.971 0.907-0.939 0.964-0.986 0.94-0.972 0.956-0.97 0.943-0.9610.955-0.962 0.97 -0.976 | 0.95 -0.967 | 0.959-0.965 | 0.927 -0.949 | |||||||
| iliacus | 0.974 -0.976 0.94 -0.96 | 0.986-0.989 0.967 -0.9790.965 -0.971 0.958-0.97 0.967 -0.971 0.976 -0.98 | 0.964 -0.974 | 0.971 -0.975 | 0.94 -0.964 | ||||||
| obturator externus | 0.927-0.934 0.881-0.904 0.945-0.96 0.893-0.9210.901-0.9240.918-0.9290.924-0.9390.916-0.938 0.92-0.931 | 0.919-0.931 | 0.893 -0.913 | ||||||||
| obturator internus | 0.881 -0.889 0.815 -0.855 0.895-0.919 0.836 -0.865 0.863 -0.8850.869-0.8890.889 -0.902 0.875-0.891 | 0.85 -0.865 | 0.803 -0.855 | ||||||||
| pectineus | 0.946-0.952 0.894-0.923 0.965-0.974 0.927 -0.9520.908 -0.933 0.932-0.947 0.943-0.95 0.952 -0.963 0.948 | 0.943 -0.947 | 0.883 -0.924 | ||||||||
| pelvis | 0.978-0.981 0.952-0.964 0.989-0.992 0.968-0.9820.969-0.977 0.968-0.9740.979-0.981 0.98-0.984 0.971-0.977 | 0.978 -0.983 | 0.956 -0.967 | ||||||||
| phalangeal extensors | 0.948-0.958 0.879-0.912 0.967-0.977 0.946-0.9570.942-0.954 0.917-0.9390.896-0.9630.953-0.959 0.932-0.947 | 0.918 -0.96 | 0.892 -0.938 | ||||||||
| piriformis | 0.917 -0.936 0.88 -0.908 0.938 -0.932 0.894 -0.924 0.917 -0.9290.919 -0.9350.883 -0.949 0.923 -0.932 | 0.914 -0.927 | 0.873 -0.912 | ||||||||
| popliteus | 0.863-0.889 0.846-0.865 0.889-0.911 0.852-0.8680.873-0.887 0.849-0.89 0.857-0.8760.895-0.906 0.854-0.895 | 0.84 -0.852 | 0.837 -0.854 | ||||||||
| psoas major | 0.98-0.982 0.943-0.965 0.989-0.974-0.984 0.976-0.98 0.961-0.9740.975-0.9790.979-0.984 0.969-0.98 | 0.978-0.982 | 0.944 -0.97 | ||||||||
| quadratus femoris | 0.905-0.915 0.774-0.833 0.859-0.962 0.871-0.9030.876-0.908 0.848-0.8870.867-0.9070.914-0.925 0.878-0.907 | 0.884 -0.9 | 0.759-0.833 | ||||||||
| quadratus lumborum | 0.926-0.933 0.865-0.896 0.93-0.951 0.903-0.9240.914-0.9320.895-0.9170.922-0.9310.922-0.944 0.911-0.93 | 0.902 -0.919 | 0.87 -0.898 | ||||||||
| rectus femoris | 0.982 -0.985 0.915 -0.946 0.947 -0.982 0.972 -0.9880.949 -0.976 0.98 -0.983 0.97 -0.988 0.962 -0.976 | 0.983 -0.986 | 0.923 -0.965 | ||||||||
| sartorius | 0.974-0.976 0.928-0.952 0.984-0.987 0.958-0.9780.956-0.974 0.955-0.9670.968-0.971 0.976-0.98 0.963-0.972 | 0.97 -0.974 | 0.927 -0.958 | ||||||||
| semimembranosus | 0.983-0.985 0.912-0.959 0.991-0.993 0.934-0.9940.955-0.976 0.969-0.979 0.975-0.98 0.98-0.986 0.971-0.981 | 0.981 -0.984 | 0.943 -0.967 | ||||||||
| semitendinosus | 0.982-0.984 0.989-0.964 0.988-0.992 0.962-0.9870.969-0.981 0.968-0.9780.979-0.987 0.975-0.982 | 0.977 -0.981 | 0.946 -0.971 | ||||||||
| soleus | 0.982-0.986 0.937-0.958 0.993-0.995 0.983-0.99 0.968-0.981 0.962-0.9750.964-0.984 0.98 -0.985 | 0.97 -0.98 | 0.975 -0.989 | 0.943 -0.968 | |||||||
| tensor fasciae latae | 0.955-0.959 0.907-0.937 0.943-0.974 0.948-0.9560.939-0.958 0.934-0.9560.958-0.965 0.939-0.957 | 0.94 -0.95 | 0.919 -0.942 | ||||||||
| tibia | 0.98 -0.984 0.966 -0.974 0.993 -0.994 0.976 -0.983 0.979 -0.984 0.975 -0.98 0.958 -0.986 -0.98 -0.98 -0.98 -0.98 - | 0.966 -0.983 | 0.966 -0.976 | ||||||||
| tibialis anterior | 0.965-0.972 0.914-0.94 0.979-0.985 0.964-0.9710.959-0.969 0.941-0.96 0.931-0.972 0.96-0.977 0.956-0.967 | 0.944 -0.97 | 0.917 -0.955 | ||||||||
| tibialis posterior | 0.957-0.965 0.938-0.95 0.973-0.98 0.953-0.965 0.955-0.96 0.954-0.9610.912-0.968 0.957-0.962 | 0.932 -0.968 | 0.941 -0.954 | ||||||||
| vastus intermedius | 0.98 -0.981 | 0.91 -0.955 | 0.99 -0.994 0.925 -0.992 0.958 -0.978 0.961 -0.9710.971 -0.976 0.97 -0.985 0.97 -0.977 | 0.977 -0.982 | 0.942 -0.962 | ||||||
| vastus lateralis | 0.991 -0.992 0.96 -0.979 0.992 -0.998 0.97 -0.997 0.982 -0.987 0.987 -0.99 0.985 -0.994 0.984 -0.989 | 0.991 -0.994 | 0.977 -0.984 | ||||||||
| vastus medialis | 0.988-0.99 0.947-0.973 0.962-0.995 0.968-0.9980.975-0.986 0.977-0.9870.984-0.993 0.979-0.987 | 0.988 -0.99 | 0.965-0.978 |
13
| Table 5: Volume difference (ml) 95% confidence interval for all 80 musculoskeletal structures across | ||
|---|---|---|
| all subgroup analyses. |
| Descriptive Statistics (VDt) ml 95% Confidence Interval | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| ROI | Pathology Level | ||||||||||
| Subgroup Analysis | MRI Manufacturer | ||||||||||
| Subgroup Analysis | Age/Biologic Sex | ||||||||||
| Subgroup Analysis | |||||||||||
| Healthy | Patient | Canon | GE | Philips | Siemens | Toshiba | Years | Years | years | Male > 21 Male 18-21 Female > 21 Female 18-21 | |
| years | |||||||||||
| adductor brevis | 1.09 -1.74 | 1.51 -2.29 | 0-0.09 | 0.7 -1.52 | 0.98 -1.98 | 1.54 -2.41 | 0.6 -0.94 | 1.21 -2 | 1.19 -1.77 | 0.09 -2.46 | 1.28-2.57 |
| adductor longus | 1.5 -2.26 | 1.52 -2.59 | 0.08 -2.17 | 1.08 -2.52 | 1.93 -3.18 | 1.48 -2.47 | 0.62 -1.08 | 1.37 -2.54 | 1.69 -2.45 | 0.19 -2.31 | 0.88 -2.79 |
| adductor magnus | 3.43 -5.45 | 3.74 -5.62 | 0.53 -1.84 | 2.96 -5.6 | 2.5 -5.58 | 3.75 -6.24 | 2.37 -6.3 | 2.6 -4.76 | 3.66 -5.42 | 2.16 -9.98 | 2.6 -4.36 |
| biceps femoris: long head | 1.13 -1.72 | 2.28 -4.2 | 0.13 -1.4 | 1.18 -3.66 | 1.41 -2.25 | 1.54 -2.59 | 0.79 -1.47 | 1 -2.38 | 1.52 -2.51 | 0.34 -2.15 | 1.21 -3.44 |
| biceps femoris: short head | 0.79 -2.28 | 1.86 -3.38 | 0-0.09 | 1.07 -6.66 | 0.86 -1.28 | 1.34 -2.06 | 1.08 -1.99 | 1.28-2.16 | -1.03 -6.24 | 1 -2.11 | |
| external rotators | 0.72 -0.98 | 0.73 -1.1 | 0.16 -0.89 | 0.47 -0.92 | 0.7 -1.27 | 0.81 -1.11 | 0.37 -0.84 | 0.76 -1.31 | 0.8 -1.1 | 0.33 -0.74 | 0.43 -0.89 |
| femur | 1.58 -5.2 | 2.07 -3.04 | -3.97 -43.01 0.23 -11.13 | 1.42 -2.27 | 1.76 -2.55 | 1.26 -1.99 | 1.49 -4.51 | -0.64 -13.83 | 1.76 -3.08 | ||
| fibula | 0.52 -0.77 | 2.37 -4.27 | 0.06 -0.22 | 1.18 -3.27 | 0.45 -0.77 | 1.09 -1.77 | 0.28 -0.49 | 0.57 -1.22 | 1.07 -2.14 | 0.29 -0.51 | 0.75 -1.92 |
| fibulari | 1.49 -2.47 | 2.75 -5.28 | 0.09 -1.95 | 1.43 -3.76 | 1.7 -2.53 | 1.91 -3.71 | 1.54 -2.47 | 1.04 -4.11 | 2.04 -3.27 | 1.01 -1.8 | 1.46 -4.35 |
| flexor digitorum longus | 0.65 -1.21 | 0.97 -1.88 | 0.04 -0.54 | 0.66 -1.44 | 0.48 -0.83 | 0.91 -1.86 | 0.39 -0.76 | 0.65 -2.49 | 0.71 -1.2 | 0.41 -0.79 | 0.65 -1.51 |
| flexor hallucis longus | 0.93 -1.57 | 1.81 -3.52 | 0.12 -0.46 | 0.93 -2.27 | 0.69 -2.83 | 1.32 -2.23 0.56 -1.38 | 0.8-1.48 | 1.36-2.51 | 0.29 -0.88 | 1.09 -3.2 | |
| Gastrocnemius: lateral head | 1.35 -2 | 2.41 -4.07 | 0-6.67 | 1.32 -3.03 | 1.34 -1.87 | 1.85 -2.97 | 0.78-1.79 | 1.22 -2.16 | 1.77 -2.88 | 0.68 -1.92 | 1.55 -3.42 |
| Gastrocnemius: medial head | 1.18 -3.03 | 4.05 -9.07 | -2.85 -33.17 1.47 -7.55 | 1.11 -1.68 | 2.34 -4.27 | 0.2 -3.12 | 1-1.78 | 2.32 -6.16 | 0.46 -3.39 | 2.05 -6 | |
| gluteus maximus | 4.2 -6.04 | 8.76 -21.66 | 0.04 -3.28 | 0.23 -14.63 | 4.05 -7.74 | 7.24-12.78 1.22 -3.07 | 4.69 -7.2 | 5.37 -11.23 | 0.69 -2.42 | 6.05 -12.05 | |
| gluteus medius | 1.27 -2.29 | 2.22 -3.81 | 0.02 -0.34 | 0.98 -2.28 | 1.46 -2.61 | 1.93 -3.49 | 0.56 -1.11 | 1.54 -3 | 1.65 -3.12 | -0.08 -2.27 | 1.43 -2.41 |
| gluteus minimus | 1-1.38 | 1.54 -2.4 | 0.53 -1.8 | 0.93 -2.04 | 1.06 -1.7 | 1.27 -1.82 | 0.51 -0.9 | 0.98 -1.55 | 1.23 -1.77 | 0.36 -0.85 | 1.12 -2.27 |
| gracilis | 0.77 -1.3 | 1.63 -3.19 | -0.03 -0.5 | 0.71 -2.27 | 0.74 -1.21 | 1.25 -2.28 | 0.7 -1.24 | 0.69 -1.2 | 1.1 -1.94 | 0.2 -2.43 | 0.96 -2.82 |
| iliacus | 1.49 -2.18 | 2.44 -4.44 | 0.08 -0.82 | 1.23 -3.38 | 1.98 -3.37 | 1.94 -3.13 | 0.53 -0.98 | 1.28 -2.42 | 2.17 -3.2 | 0.09 -1.93 | 1.02 -3.8 |
| obturator externus | 1.78 -2.53 | 1.99 -3.08 | 0.2 -1.5 | 1.3 -2.34 | 1.71 -2.93 | 2.09 -3.09 | 0.74-2.69 | 1.78-3.55 | 1.88 -2.72 | 1 -2.41 | 1.48 -2.9 |
| obturator internus | 0.64 -0.9 | 1.01 -1.77 | -0.04 -0.47 | 0.82 -1.96 | 0.74 -1.28 | 0.72 -1.03 | 0.35 -0.74 | 0.7 -1.23 | 0.85 -1.33 | 0.24 -0.74 | 0.47 -1.11 |
| pectineus | 1.55 -2.71 | 1.62 -2.58 | -0.05 -0.72 | 0.66 -1.84 | 2.47 -5 | 1.54 -2.8 | 0.32 -1.02 | 0.9 -1.87 | 2.06 -3.6 | 0.1 -2.31 | 1.22 -2.25 |
| pelvis | 1.66 -2.38 | 2.53 -3.98 | 0.45 -1.49 | 1.25 -2.75 | 1.47 -2.39 | 2.49 -3.61 | 0.59 -1.26 | 1.16 -2.65 | 2.21 -3.21 | 0.7 -2.35 | 1.75 -3.06 |
| phalangeal extensors | 1.24 -1.59 | 1.84 -3 | 0.18 -0.75 | 1.28 -2.45 | 0.92 -1.49 | 1.68 -2.28 0.86 -1.31 | 1.19 -1.89 | 1.49 -2.15 | 0.83 -1.19 | 1.25 -2.48 | |
| piriformis | 1.09 -1.72 | 1.64 -2.8 | -0.1 -1.15 | 1.05 -3.26 | 0.87 -1.53 | 1.4 -2.08 | 0.82 -1.98 | 0.8 -2.82 | 1.15 -1.71 | 1.04 -1.89 | 1.16 -2.73 |
| popliteus | 0.26 -0.64 | 0.4 -0.64 | -0.01 -0.03 | 0.29 -0.64 | 0.27 -0.41 | 0.31 -0.86 | 0.21 -0.36 | 0.34 -0.54 | 0.3 -0.88 | 0.15 -0.27 | 0.26 -0.42 |
| psoas major | 2.29 -3.39 | 2.31 -6.09 | 0.29 -1.45 | 1.57 -4.75 | 2.49 -4.77 | 2.51 -4.75 | 0.58-1.33 | 2.4 -4.77 | 2.41 -4.07 | 0.85 -2.32 | 1.09 -6.89 |
| quadratus femoris | 0.79 -1.1 | 0.99 -1.81 | 0.33 -1.19 | 0.68 -1.21 | 0.72 -1.22 | 1.01 -1.59 | 0.29 -0.58 | 0.79 -1.31 | 0.92 -1.24 | 0.26 -1.34 | 0.59 -1.95 |
| quadratus lumborum | 1.78 -2.25 | 2.35 -5.13 | 1.41 -3.92 | 2.06 -3.75 | 1.63 -2.45 | 1.96 -3.71 | 0.77 -1.58 | 1.4 -2.34 | 1.84 -3.47 | 1.38 -2.5 | 1.88 -3.85 |
| rectus femoris | 1.98 -3.59 | 3.47 -6.14 | -5.19 -20.36 1.29 -5.04 | 2.41 -3.55 | 2.83 -4.69 | 0.8 -1.66 | 1.59 -4.31 | 2.37 -4.44 | 0.41 -4.4 | 2.71 -5.85 | |
| sartorius | 1.37 -2.01 | 2.66 -5.16 | 0.06 -0.73 | 1.27 -4.35 | 1.79 -3.37 | 1.89 -3.07 | 0.45 -1 | 1.05 -3.06 | 1.85 -3.11 | 0.19 -1.86 | 1.83 -4.09 |
| semimembranosus | 1.69 -2.93 | 3.28-6.43 | 0.01 -0.71 | 0.9-3.64 | 1.84 -3.49 | 2.45 -4.58 1.19 -4.41 | 1.5 -2.62 | 2.21 -4.07 | 0.67 -4.61 | 1.5 -5.07 | |
| semitendinosus | 1.33 -1.99 | 2.37 -4.13 | 0.02 -0.57 | 0.5 -1.78 | 1.79 -2.86 | 1.96 -3.14 | 0.92 -1.49 | 0.87 -2.45 | 1.82 -2.75 | 0.61 -1.18 | 1.45 -3.65 |
| soleus | 2.23 -3.76 | 6.99 -12.81 -1.74 -12.49 | 2.41 -7.78 | 1.98 -3.6 | 3.91 -7.1 | 2.25 -5.58 | 2.1 -5 | 3.37 -6.51 | 0.97 -5.96 | 3.72 -9.58 | |
| tensor fasciae latae | 1.06 -1.51 | 1.77 -3.34 | -0.25 -3.22 | 1.35 -3.02 | 0.65 -1.38 | 1.47 -2.33 | 0.39 -0.88 | 0.79 -1.59 | 1.5 -2.37 | 0.46 -1.39 | 0.91 -2.44 |
| tibia | 1.96 -3.16 | 2.87 -5.08 | -3.1 -10.73 | 2.1 -4.12 | 1.4 -4.15 | 2.43 -3.92 | 0.79 -1.68 | 1.66 -3.14 | 2.33 -4.1 | 1.24 -2.65 | 1.96 -5.72 |
| tibialis anterior | 0.82 -1.88 | 1.71 -2.75 | -5.14 -17.75 | 0.68 -1.77 | 0.84 -1.47 | 1.32 -1.92 | 1.11 -1.7 | 0.83 -1.73 | 0.95 -2.69 | 0.71 -1.24 | 1.25 -2.25 |
| tibialis posterior | 1.09 -1.48 | 1.27 -2.16 | 0.24 -0.61 | 0.7 -1.72 | 1.34 -2.03 | 1.31 -1.85 -0.45 -0.97 | 1.48 -2.34 | 1.18-1.73 | 0.34 -1.09 | 0.85 -1.68 | |
| vastus intermedius | 2.05 -2.73 | 2.46 -4.12 | 0.04 -0.54 | 0.97 -3.11 | 2.05 -3.35 | 2.55 -3.48 | 1.81 -3.41 | 2.1 -3.36 | 2.32 -3.22 | 1.04 -2.55 | 1.57 -3.49 |
| vastus lateralis | 3.72 -5.69 | 5.57 -9.23 | 0.19-1.52 | 1.97 -6.94 | 4.54 -6.35 | 5.03 -7.73 | 1.3 -7.45 | 3.39 -7.21 | 3.79 -5.23 | 1.78 -9.16 | 4.81 -8.79 |
| vastus medialis | 2.08 -4.39 | 3.17 -6.29 | -3.01 -11.05 | 1.21 -7.64 | 2.33 -3.48 | 2.36 -3.87 -0.26 -13.14 1.64 -3.29 | 2.04 -3.35 | 1.43 -12.16 | 2.2 -5.5 | ||
14
9. Substantial Equivalence Conclusion
In conclusion, MuscleView is intended for use in adults and pediatric patients aged 18 and older to quantify automatically segment muscle and bone structures of the lower extremities from magnetic resonance imaging and provide derived metrics including muscle volume, bone volume, intramuscular fat percentage, and left/right asymmetry and other derived metrics. It has similar intended use and indications for use statement as the AMRA Profiler (510k Number: K173749). This 510(k) submission includes information on the MuscleView technological characteristics, as well as performance data and verification and validation activities demonstrating that MuscleView is as safe and effective as the AMRA Profiler (510k Number: K173749), and does not raise different questions of safety and effectiveness.