(158 days)
Not Found
Yes
The document explicitly states, "The BoneMRI application uses an algorithm derived from machine learning (ML)..." and mentions "Re-training to improve ML model performance".
No.
The device is an image processing software for enhancing MRI images to visualize bone structures, not for direct treatment or therapy.
No
Explanation: The "Intended Use / Indications for Use" section explicitly states, "BoneMRI is not to be used for diagnosis or monitoring of (primary or metastatic) tumors." While it processes images for enhancement, it disclaims diagnostic use.
Yes
The device is explicitly described as a "standalone image processing software application" and a "server application running on the clinic or hospital networks." While it has hardware requirements (specific GPU) and can be deployed on-premise or as a cloud service, the core medical device functionality is the software itself that processes MRI images. The description focuses entirely on the software's function, algorithms, and data handling, without mentioning any proprietary hardware components that are part of the medical device itself.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections. They are used to provide information for diagnosis, monitoring, or screening.
- BoneMRI's Function: BoneMRI is an image processing software that enhances existing MRI images to visualize bone structures. It does not analyze biological samples from the body. It works with medical images already acquired from the patient.
- Intended Use: The intended use is for image enhancement and visualization of bone structures in MRI images, not for performing diagnostic tests on biological samples.
- Device Description: The device description clearly states it's a software application that analyzes 3D gradient echo MRI scans.
Therefore, BoneMRI falls under the category of medical imaging software or image processing software, not an In Vitro Diagnostic device.
Yes
The last section of the provided text, titled "Predetermined Change Control Plan (PCCP) - All Relevant Information," explicitly states: "FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP)." It then details the specific modifications covered under this PCCP, such as re-training to improve ML model performance and validation of additional scanner support.
Intended Use / Indications for Use
BoneMRI is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the bone structures in MRI images with increased contrast with respect to the surrounding soft tissue. It is to be used in the pelvic region, which includes the bony anatomy of the sacrum, hip bones and the spine, which includes the bony anatomy of the cervical, thoracic, lumbar, and S1 vertebrae. BoneMRI is indicated for use in patients 12 years and older.
BoneMRI is not to be used for diagnosis or monitoring of (primary or metastatic) tumors. BoneMRI images are not intended to replace CT images in general but can be used to visualize 3D bone morphology, tissue radiodensity and tissue radiodensity contrast.
Product codes (comma separated list FDA assigned to the subject device)
OIH
Device Description
The BoneMRI application is a standalone image processing software application that analyses 3D gradient echo MRI scans acquired with a dedicated MRI scan protocol. From the analysis of the gradient echo MRI scan, 3D tomographic radiodensity contrast images, called BoneMRI images, are constructed.
The BoneMRI images can be used to visualize the bone structures in MR images with enhanced contrast with respect to the surrounding soft tissue. The application is designed to be used by imaging experts, such as radiologists or orthopedic surgeons, typically in a physician's office.
The BoneMRI application is a server application running on the clinic or hospital networks. It is available as fully on-premise software with specific GPU hardware requirements, or partly running as a managed cloud service, for which the environment in which the managed modules run is controlled by MRIguidance. The on-premise software is fully controlled by the clinic or hospital, and as such, no protected health information (PHI) will leave the clinic or hospital network. All data sent to the managed cloud server will be de-identified before it leaves the clinic or hospital network, and as such, the managed cloud service will not receive PHI.
Within the hospital network, the application communicates with a DICOM compatible imaging archive (e.g., a PACS) to receive input MRI and to return BoneMRI images. Reading of the resulting BoneMRI images is performed using regular DICOM compatible medical image viewing software.
The BoneMRI application uses an algorithm to detect bone images from MRIs obtained using a specific gradient echo acquisition sequence. The algorithm training sets included images from multiple clinical sites, multiple anatomies, and multiple scanners to ensure that the trained algorithm was robust with respect to the approved indications for use. None of the data used in the training dataset was used subsequently in the validation dataset.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
MRI
Anatomical Site
pelvic region, which includes the bony anatomy of the sacrum, hip bones and the spine, which includes the bony anatomy of the cervical, thoracic, lumbar, and S1 vertebrae.
Indicated Patient Age Range
12 years and older.
Intended User / Care Setting
imaging experts, such as radiologists or orthopedic surgeons, typically in a physician's office.
Description of the training set, sample size, data source, and annotation protocol
The algorithm training sets included images from multiple clinical sites, multiple anatomies, and multiple scanners
Description of the test set, sample size, data source, and annotation protocol
A quantitative voxel-by-voxel validation of BoneMRI was performed on imaging data from 76 patients (pelvic region) and 117 patients (spine region), consisting of the BoneMRI and CT of the same patient in the same anatomical region, acquired using standard of care bone imaging protocols during previously conducted clinical investigations. Test data are acquired at different medical sites, departments or within different clinical studies than training data, and test data is unseen data that was not used in any way during developments. The validation was conducted by MRIguidance based on an algorithm to detect bone images from MRIs obtained using a specific gradient echo sequence.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Software verification and validation testing,
Studies that utilized retrospective clinical data to demonstrate the software enhanced imaging quality in MR images via an enhancement of bone validated with CT.
A quantitative voxel-by-voxel validation of BoneMRI was performed on imaging data from 76 patients (pelvic region) and 117 patients (spine region), consisting of the BoneMRI and CT of the same patient in the same anatomical region, acquired using standard of care bone imaging protocols during previously conducted clinical investigations.
Key Results:
- accurately reconstruct the 3D bone morphology with a mean absolute cortical delineation error below 1.0 mm on average;
- accurately reconstructs the tissue radiodensity, with a mean deviation below 25 HU on average and a mean deviation below 55 HU specifically for bone;
- accurately reconstructs the tissue radiodensity contrast, with a mean HU correlation coefficient above 0.75 specifically for bone.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Cortical delineation error (mm): 0.75
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Addition of a Predetermined Change Control Plan to support an iterative development approach for the machine learning models in the BoneMRI application.
Scope:
- Re-training to improve ML model performance with additional training data: Re-training of the ML model with additional data to increase the safety and performance of the device in any of the following categories: Increased accuracy; Increased performance for challenging cases such as rare pathologies or artifacts; Increased robustness and generalization of the model.
- Validation of additional scanner support: Validation of the ML model (either with or without additional re-training of the ML model) in order to validate an additional MRI vendor or field strength.
Restrictions:
In accordance with the PCCP, all algorithm modifications will be trained, tuned, and locked prior to release of the application.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
March 1, 2024
MRIguidance B.V % Sujith Shetty Executive Vice President Maxis Medical 3031 Tisch Way Suite 1010 San Jose, California 95128
Re: K233030
Trade/Device Name: BoneMRI Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: OIH Dated: January 17, 2024 Received: January 22, 2024
Dear Sujith Shetty:
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.
1
FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device. or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.
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).
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 QS 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.
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.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn
2
(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-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) K233030
Device Name BoneMRI
Indications for Use (Describe)
BoneMRI is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the bone structures in MRI images with increased contrast with respect to the surrounding soft tissue. It is to be used in the pelvic region, which includes the bony anatomy of the sacrum, hip bones and the spine, which includes the bony anatomy of the cervical, thoracic, lumbar, and S1 vertebrae. BoneMRI is indicated for use in patients 12 years and older.
BoneMRI is not to be used for diagnosis or monitoring of (primary or metastatic) tumors. BoneMRI images are not intended to replace CT images in general but can be used to visualize 3D bone morphology, tissue radiodensity and tissue radiodensity contrast.
| 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)
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Image /page/4/Picture/1 description: The image shows the logo for MRI Guidance. The logo consists of a circle with a right-angle arrow inside it, followed by the text "MRI guidance". The circle and the "MRI" text are in a gradient of light blue and green, while the "guidance" text is in gray.
510(k) Summary
1. 510(k) Information
| 510(k) Number: | K233030 |
|---|---|
| Date Prepared: | February 26, 2024 |
| 510(k) Submitter: | MRIguidance B.V. |
| Maliesingel 23, 3581 BG | |
| Utrecht, the Netherlands | |
| 510(k) Submitter Contact Person: | David Sparks |
| Head of Regulatory Affairs | |
| MRIguidance B.V. | |
| Email: david.sparks@mriguidance.com | |
| Tel: +31 681741711 | |
| Correspondent Contact Person: | Dr. Sujith Shetty |
| EVP Maxis Medical | |
| 3031 Tische Way, Suite 1010 | |
| San Jose, CA 95128 USA | |
| Email: sjshetty@maxismedical.com |
2. Device Information
| Device Trade Name: | BoneMRI |
|---|---|
| Device Common Name: | MRI image enhancement software |
| Device Classification Name: | Medical image management and processing system (21 |
| CFR 892.2050) | |
| Device Classification: | Class II |
| Product Code: | QIH |
3. Predicate Device
| Device Trade Name: | BoneMRI |
|---|---|
| Manufacturer: | MRIguidance B.V. |
| Device 510(k) Clearance: | K230197 |
| Device Classification Name: | Medical image management and processing system (21 |
| CFR 892.2050) | |
| Device Classification: | Class II |
| Product Code: | QIH |
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Image /page/5/Picture/1 description: The image shows the logo for MRI Guidance. The logo consists of a circle with a right angle symbol inside of it on the left, and the text "MRI guidance" on the right. The word "MRI" is in a blue color, and the word "guidance" is in a gray color.
4. Device Description
The BoneMRI application is a standalone image processing software application that analyses 3D gradient echo MRI scans acquired with a dedicated MRI scan protocol. From the analysis of the gradient echo MRI scan, 3D tomographic radiodensity contrast images, called BoneMRI images, are constructed.
The BoneMRI images can be used to visualize the bone structures in MR images with enhanced contrast with respect to the surrounding soft tissue. The application is designed to be used by imaging experts, such as radiologists or orthopedic surgeons, typically in a physician's office.
The BoneMRI application is a server application running on the clinic or hospital networks. It is available as fully on-premise software with specific GPU hardware requirements, or partly running as a managed cloud service, for which the environment in which the managed modules run is controlled by MRIguidance. The on-premise software is fully controlled by the clinic or hospital, and as such, no protected health information (PHI) will leave the clinic or hospital network. All data sent to the managed cloud server will be de-identified before it leaves the clinic or hospital network, and as such, the managed cloud service will not receive PHI.
Within the hospital network, the application communicates with a DICOM compatible imaging archive (e.g., a PACS) to receive input MRI and to return BoneMRI images. Reading of the resulting BoneMRI images is performed using regular DICOM compatible medical image viewing software.
The BoneMRI application uses an algorithm to detect bone images from MRIs obtained using a specific gradient echo acquisition sequence. The algorithm training sets included images from multiple clinical sites, multiple anatomies, and multiple scanners to ensure that the trained algorithm was robust with respect to the approved indications for use. None of the data used in the training dataset was used subsequently in the validation dataset.
5. Indications for Use
BoneMRI is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the bone structures in MRI images with increased contrast with respect to the surrounding soft tissue. It is to be used in the pelvic region, which includes the bony anatomy of the sacrum, hip bones and femoral heads; and the spine, which includes the bony anatomy of the cervical, thoracic, lumbar, and S1 vertebrae. BoneMRI is indicated for use in patients 12 years and older.
BoneMRI is not to be used for diagnosis or monitoring of (primary or metastatic) tumors. BoneMRI images are not intended to replace CT images in general but can be used to visualize 3D bone morphology, tissue radiodensity and tissue radiodensity contrast.
6. Comparison of Technological Characteristics with the Predicate Device
A comparison of the intended use, indication for use, and technological characteristics of the subject BoneMRI application to the predicate device (BoneMRI v1.6, K230197) is presented below. We have included the attributes suggested in the July 2018 Guidance "The 510(k) Program: Evaluating Substantial Equivalence in Premarket Notifications" for this comparison.
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Image /page/6/Picture/1 description: The image contains the logo for MRI Guidance. The logo consists of a circular icon with a stylized arrow pointing upwards and to the right, colored in a gradient of light blue and green. To the right of the icon, the text "MRI" is displayed in a similar gradient of light blue and green, while the word "guidance" is written in gray.
Table 1 Predicate device comparison
| | Predicate Device
(BoneMRI K230197) | Subject Device
(BoneMRI) | Comment |
|-------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------|
| Intended Use | BoneMRI is an image
processing software that can
be used for image
enhancement in MRI images.
It can be used to visualize the
bone structures in MRI
images with enhanced
contrast with respect to the
surrounding soft tissue. | BoneMRI is an image
processing software that can
be used for image
enhancement in MRI images.
It can be used to visualize the
bone structures in MRI
images with enhanced
contrast with respect to the
surrounding soft tissue. | The same |
| 21CFR Section | 829.2050 | 829.2050 | The same |
| Product Code | QIH | QIH | The same |
| Target
Population | Adults | Adolescents and Adults | Different |
| Indications for
Use | BoneMRI is an image
processing software that can
be used for image
enhancement in MRI images.
It can be used to visualize the
bone structures in MRI
images with enhanced
contrast with respect to the
surrounding soft tissue. It is
to be used in the pelvic
region, which includes the
bony anatomy of the sacrum,
hip bones and femoral heads;
and the lumbar spine region,
which includes the bony
anatomy of the vertebrae
from L3 to S1. BoneMRI is
not to be used for diagnosis
or monitoring of (primary or
metastatic) tumors.
Warning: BoneMRI images
are not intended to replace
CT images. | BoneMRI is an image
processing software that can
be used for image
enhancement in MRI images.
It can be used to visualize the
bone structures in MRI
images with increased
contrast with respect to the
surrounding soft tissue. It is
to be used in the pelvic
region, which includes the
bony anatomy of the sacrum,
hip bones and femoral heads;
and the spine, which includes
the bony anatomy of the
cervical, thoracic, lumbar, and
S1 vertebrae. BoneMRI is
indicated for use in patients
12 years and older.
BoneMRI is not to be used
for diagnosis or monitoring of
(primary or metastatic)
tumors. BoneMRI images are
not intended to replace CT
images in general but can be
used to visualize 3D bone
morphology, tissue
radiodensity and tissue
radiodensity contrast. | Similar |
| Technological Characteristics | | | |
| Device Nature | Software package | Software package | The same |
| Operating System | Linux | Linux | The same |
| Data Input | MRI images in DICOM format | MRI images in DICOM format | The same |
| Data Output | MRI images in DICOM format | MRI images in DICOM format | The same |
| Processing Algorithms | MRIguidance software
implements an image
enhancement algorithm using
convolutional neural network.
Original images are enhanced
by running them through a
cascade of filter banks, where
thresholding and scaling
operations are applied.
Separate neural network-
based filters are obtained to
assign a Hounsfield Unit (HU)
value to a single volume
element, based on intensity
and contextual information.
The parameters of the model
were obtained through an
algorithm development
pipeline. | MRIguidance software
implements an image
enhancement algorithm using
convolutional neural network.
Original images are enhanced
by running them through a
cascade of filter banks, where
thresholding and scaling
operations are applied.
Separate neural network-
based filters are obtained to
assign a Hounsfield Unit (HU)
value to a single volume
element, based on intensity
and contextual information.
The parameters of the model
were obtained through an
algorithm development
pipeline. | The same |
| User Interface | None - enhanced images are
viewed on existing PACS
workstations. | None – enhanced images are
viewed on existing PACS
workstations. | The same |
| Workflow | The software operates on
DICOM files on the file
system, enhances the images,
and stores the enhanced
images on the file system.
The receipt of original
DICOM image files and
delivery of enhanced images
as DICOM files depends on
other software systems.
Enhanced images co-exist
with the original images. | The software operates on
DICOM files on the file
system, enhances the images,
and stores the enhanced
images on the file system.
The receipt of original
DICOM image files and
delivery of enhanced images
as DICOM files depends on
other software systems.
Enhanced images co-exist
with the original images. | The same |
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Image /page/7/Picture/1 description: The image is a logo for MRI Guidance. The logo consists of a circle with a right angle symbol inside of it, followed by the text "MRI guidance". The text "MRI" is in a larger font than the word "guidance". The colors of the logo are a gradient of light blue and green.
7. Summary of Changes
The changes to the BoneMRI application from the predicate device (BoneMRI v1.6, K230197) to the subject device are detailed in the table below.
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Image /page/8/Picture/1 description: The image contains the logo for MRI Guidance. The logo consists of a green circle with a white arrow pointing upwards and to the right. To the right of the circle, the text "MRI" is written in a blue gradient, and below that, the text "guidance" is written in gray.
Table 2 Summary of changes for the BoneMRI application
| Change | Change Description |
|---|---|
| Software architecture | Refactor of the application workflow; introducing multi-tenancy capabilities; and improving scalability of cloud deployment. |
| Validation strategy | Revision of the validation strategy to support the validation of a multi-vendor, multi-field strength algorithm. |
| Intended patient population | The intended patient population of BoneMRI is extended to include adolescents. The algorithm for the application has not been changed. |
| Predetermined Change Control Plan | Addition of a Predetermined Change Control Plan to support an iterative development approach for the machine learning models in the BoneMRI application. |
| Algorithm for the Spine region | The algorithm for the Spine region has been re-trained. With additional data for training and testing, the anatomical region of the algorithm was extended to include the Cervical Spine and the Thoracic Spine in addition to the Lumbar Spine. |
| Algorithm for the Pelvic region | No changes are made to the algorithm for the Pelvic region. Updates have been made to improve statistical testing of the algorithm and to test the algorithm on additional subgroups. |
8. Predetermined Change Control Plan
The BoneMRI application uses an algorithm derived from machine learning (ML) to detect bone images from MRIs obtained using a specific gradient echo acquisition sequence. The algorithm training sets included images from multiple clinical sites, multiple anatomies, and multiple scanners to ensure that the trained algorithm was robust with respect to the approved indications for use. MRIguidance will make future algorithm improvements under a Predetermined Change Control Plan (PCCP). In that plan, a protocol is provided to mitigate the risks of the algorithm changes leading to changes in the device's technical specifications or negatively affecting performance specifications directly associated with the indications for use of the device. Changes made under this PCCP are detailed in the table below. In accordance with the PCCP, all algorithm modifications will be trained, tuned, and locked prior to release of the application.
| Modification | Rationale |
|---|---|
| 1. Re-training to improve ML | |
| model performance with | |
| additional training data | Re-training of the ML model with additional data to increase the |
| safety and performance of the device in any of the following | |
| categories: | |
| Increased accuracy; Increased performance for challenging cases such as rare | |
| pathologies or artifacts; Increased robustness and generalization of the model. | |
| 2. Validation of additional | |
| scanner support | Validation of the ML model (either with or without additional re- |
| training of the ML model) in order to validate an additional MRI | |
| vendor or field strength. |
Table 3 Summary of changes under a Predetermined Change Control Plan
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Image /page/9/Picture/1 description: The image contains the logo for MRI Guidance. The logo consists of a circle with an arrow pointing to the upper right, and the text "MRI guidance" to the right of the circle. The word "MRI" is in a larger font size and a gradient color, while the word "guidance" is in a smaller font size and a gray color.
9. Performance Data
The following performance testing has been performed on BoneMRI:
-
- Software verification and validation testing
- Studies that utilized retrospective clinical data to demonstrate the software enhanced imaging 2. quality in MR images via an enhancement of bone validated with CT.
Software Verification and Validation Testing
Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" dated June 14, 2023.
Performance Validation
A quantitative voxel-by-voxel validation of BoneMRI was performed on imaging data from 76 patients (pelvic region) and 117 patients (spine region), consisting of the BoneMRI and CT of the same patient in the same anatomical region, acquired using standard of care bone imaging protocols during previously conducted clinical investigations. Test data are acquired at different medical sites, departments or within different clinical studies than training data, and test data is unseen data that was not used in any way during developments. The validation was conducted by MRIguidance based on an algorithm to detect bone images from MRIs obtained using a specific gradient echo sequence. The demographics and performance data, including subgroup analysis of the patient population are described in the table below.
| Subgroup | N | Gender | Age | Data
origin | Cortical delineation
error (mm) | Mean deviation
in all tissue and bone
(HU) | Correlation coefficient
in bone |
|----------|-----|----------------|-------------|----------------|------------------------------------|--------------------------------------------------|------------------------------------|
| Pelvis | 76 | 75% M
25% F | $53 \pm 26$ | US,
EU | 0.75 |
| Spine | 117 | 49% M
51% F | $48 \pm 23$ | US,
EU | 0.75 |
| Pelvis | 57 | 93% M
7% F | $66 \pm 17$ | US,
EU | 0.75 |
| Spine | 94 | 51% M
49% F | $59 \pm 16$ | US,
EU | 0.75 |
| Pelvis | 19 | 21% M
79% F | $17 \pm 3$ | US | 0.75 |
| Spine | 23 | 22% M
78% F | $15 \pm 2$ | EU | 0.75 |
Table 4 Validation data demographics and performance testing was with a significance level of p 0.75 |
|-----|------------------|----|----------------|---------|-----------|--------|--------------------|--------|
| | Spine | 23 | 33% M
67% F | 64 ± 10 | US | 0.75 |
| EU | Pelvis | 51 | 100% M
0% F | 71 ± 80 | EU | 0.75 |
| | Spine | 91 | 46% M
54% F | 49 ± 23 | EU | 0.75 |
| BMI | Obese | 11 | 64% M
36% F | 51 ± 16 | US,
EU | 0.75 |
| | Over-
weight | 17 | 60% M
40% F | 52 ± 18 | US,
EU | 0.75 |
| | Healthy | 18 | 22% M
78% F | 29 ± 22 | US,
EU | 0.75 |
| | Under-
weight | 10 | 20% M
80% F | 17 ± 3 | EU | 0.75 |
The objective was to validate the quantitative accuracy of BoneMRI using rigorous, objective, and unbiased statistical tests comparing bone morphology, radiodensity, and radiodensity contrast in BoneMRI and CT images. The endpoints of the testing were the metrics that describe the accuracy of 3D bone morphology, radiodensity, and radiodensity contrast versus co-registered CT scans in terms of voxel-by-voxel HUs and standard deviations around these HU values. Subgroup analyses for different MRI vendors, field strengths, age ranges, geographical locations and BMI was performed as part of the testing. The results demonstrated clinically acceptable accuracy on each of these endpoints.
The data provided demonstrate that BoneMRI application can:
- accurately reconstruct the 3D bone morphology with a mean absolute cortical delineation ● error below 1.0 mm on average;
- accurately reconstructs the tissue radiodensity, with a mean deviation below 25 HU on average and a mean deviation below 55 HU specifically for bone;
- 0 accurately reconstructs the tissue radiodensity contrast, with a mean HU correlation coefficient above 0.75 specifically for bone.
The BoneMRI application demonstrates accurate bone morphology, radiodensity contrast to qualitatively and quantitatively assess the bony anatomy of the pelvic and spine regions.
10. Substantial Equivalence Conclusion
The subject BoneMRI application has the same intended use and a similar indication as the identified predicate device, its predecessor (BoneMRI, K230197). The technological features of the BoneMRI application are the same as the identified predicate device. Therefore, we conclude that the BoneMRI application is substantially equivalent to the identified predicate device.