Retrospective clinical data from 164 subjects was used to validate the performance of the AI/ML algorithm by comparing device-generated bone reconstructions against CT-based reference segmentations.
Retrospective dataset; AI/ML validation; Clinical imaging data
Clinical Evidence
Study Design
Population
Comparator
Key Endpoints
Retrospective Performance Validation; Retrospective cohort study
Adult subjects (18+ years); Sample Size: 164 subjects; Number of Sites: Multiple clinical sites (US and Israel)
CT-based reference segmentations (ground truth)
Mean absolute error (MAE), Dice similarity coefficient, bone tissue radiodensity, and bone tissue radiodensity-contrast
Retrospective dataset of 164 adult subjects with paired MRI and CT scans.
—
Indications for Use
MRI2CT NextMR Spine is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the 3D bone morphology in MRI images. It is to be used in the lumbar spine vertebrae (L1-L5) and the sacrum. MRI2CT NextMR Spine is indicated for use in patients 18 years and older. MRI2CT NextMR Spine images are not intended to replace CT images in general. MRI2CT NextMR Spine is not to be used for diagnosis or monitoring of (primary or metastatic) tumors or provide a quantitative bone mineral density diagnosis or bone quality assessment.
Device Story
Cloud-based software processes standard 1.5T/3T MRI DICOM series (T1/T2-weighted) to generate enhanced 3D bone morphology visualizations of lumbar spine (L1-L5) and sacrum. Employs locked deep-learning AI/ML algorithm; no user-adjustable parameters. Operated by radiologists/orthopedic surgeons via secure web interface. Output images compatible with standard PACS/DICOM viewers. Assists clinical visualization; not for tumor diagnosis/monitoring or bone mineral density assessment. Benefits include improved bony structure visualization from MRI without replacing CT.
Clinical Evidence
Retrospective study of 164 adult subjects comparing device-generated bone reconstructions to CT-based ground truth. Metrics: mean absolute cortical delineation error <1.0 mm; Dice similarity coefficient >0.93; radiodensity mean absolute error <86 HU; radiodensity correlation >0.75. Subgroup analysis performed across BMI, gender, and region.
Technological Characteristics
Software-only; cloud-based; DICOM input/output. Employs locked deep-learning AI/ML algorithm. Compatible with 1.5T/3T MRI scanners (Siemens, Philips, GE). Cybersecurity controls include encryption in transit/at rest and secure authentication. Developed per IEC 62304 and ISO 14971.
Indications for Use
Indicated for patients 18 years and older requiring visualization of 3D bone morphology in the lumbar spine (L1-L5) and sacrum via MRI image enhancement. Contraindicated for tumor diagnosis/monitoring or quantitative bone mineral density/quality assessment.
Regulatory Classification
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.
Special Controls
*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).
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**FDA** U.S. FOOD & DRUG
ADMINISTRATION
September 4, 2026
MRI2CT, Inc.
% Dalia Givony
Dalia Givony, Regulatory & Clinical Consulting
Hativat Golani 2
Modiin, 7172157
Israel
Re: K260525
Trade/Device Name: NextMR Spine
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH
Dated: February 16, 2026
Received: February 17, 2026
Dear Dalia Givony:
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.
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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K260525 - Dalia Givony
Page 2
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 Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13485 clause 8.3 (Nonconforming product), ISO 13485 clause 8.5.2 (Corrective action), and ISO 13485 clause 8.5.3 (Preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and ISO 13485 clause 7.5) and document changes and approvals in the Medical Device File (ISO 13485 clause 4.2.3).
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 (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-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (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-devices/device-advice-comprehensive-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-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/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-devices/device-advice-comprehensive-regulatory-
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K260525 - Dalia Givony
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assistance/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,
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 | | |
| --- | --- | --- |
| Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. | K260525 | ? |
| Please provide the device trade name(s). | | ? |
| NextMR Spine | | |
| Please provide your Indications for Use below. | | ? |
| MRI2CT NextMR Spine is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the 3D bone morphology in MRI images.It is to be used in the lumbar spine vertebrae (L1-L5) and the sacrum.MRI2CT NextMR Spine is indicated for use in patients 18 years and older.MRI2CT NextMR Spine images are not intended to replace CT images in general.MRI2CT NextMR Spine is not to be used for diagnosis or monitoring of (primary or metastatic) tumors or provide a quantitative bone mineral density diagnosis or bone quality assessment. | | |
| Please select the types of uses (select one or both, as applicable). | Prescription Use (21 CFR 801 Subpart D)Over-The-Counter Use (21 CFR 801 Subpart C) | ? |
| Please select the age group(s) for which the device(s) is to be used. | Neonates/Newborns (Birth to < 29 days old)Infants (29 days old to < 2 years old)Children (2 years old to < 12 years old)Adolescents (12 years old to < 22 years old)Adults (22 years old and greater) | ? |
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| MRI2CT | Document Title | Date | Document ID | Page |
| --- | --- | --- | --- | --- |
| | MRI2CT NextMR Spine 510(k) Summary | Sep 1, 2026 | SUB-0001 Revision 03 | 1 of 10 |

MRI2CT NextMR Spine v1.0
510(k) Summary
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| MRI2CT | Document Title | Date | Document ID | Page |
| --- | --- | --- | --- | --- |
| | MRI2CT NextMR Spine 510(k) Summary | Sep 1, 2026 | SUB-0001 Revision 03 | 2 of 10 |
# 510(k) Summary
K260525
## 1. 510(k) Information
| 510k Submitter: | MRI2CT Inc 2093 Philadelphia Pike #9769 Claymont DE 19703 |
| --- | --- |
| 510(k) Submitter Contact Person: | Aviem Amossi Chief Product Officer MRI2CT aviem@mri2ct.ai +972-52-4782733 |
| Official Correspondence: | Dalia Givony Dalia Givony, Regulatory & Clinical Consulting daliagiv@gmail.com +972-54-5449235 |
## 2. Device Information
| Device Name: | NextMR Spine |
| --- | --- |
| Common or Usual 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: | K233030 |
| Device Classification Name: | Medical image management and processing system (21 CFR 892.2050) |
| Device Classification: | Class II |
| Product Code: | QIH |
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| MRI2CT | Document Title | Date | Document ID | Page |
| --- | --- | --- | --- | --- |
| | MRI2CT NextMR Spine 510(k) Summary | Sep 1, 2026 | SUB-0001 Revision 03 | 3 of 10 |
## 4. Device Description
MRI2CT NextMR Spine is a software-only, cloud-based medical device that processes magnetic resonance imaging (MRI) data in Digital Imaging and Communications in Medicine (DICOM) format and generates enhanced DICOM image series for visualization of spinal bony anatomy. The output images are compatible with standard Picture Archiving and Communication Systems (PACS) and DICOM-compliant viewing software.
The device employs a locked deep-learning-based artificial intelligence and machine learning (AI/ML) algorithm to enhance visualization of vertebral bone structures. The software does not provide diagnostic or treatment recommendations and includes no user-adjustable algorithm parameters.
MRI2CT NextMR Spine is provided as a secure, web-based application hosted on MRI2CT-managed cloud infrastructure and does not require local software installation. Authorized users, including radiologists, orthopedic surgeons, and other imaging professionals, upload MRI DICOM series to the cloud platform through an authenticated interface. Data transmission and storage are secured using encryption in transit and at rest.
The software processes standard MRI series acquired on 1.5T and 3T scanners from major MRI manufacturers (i.e., Siemens, Philips, and GE) using conventional sagittal T1-weighted or T2-weighted sequences. The device outputs an enhanced DICOM image series with improved visualization of the vertebral bony structures of the lumbar spine (L1-L5) and sacrum. The output images can be viewed in sagittal, coronal, and axial planes and as a three-dimensional (3D) reconstruction using standard DICOM-compatible viewing workstations or within the MRI2CT web interface.
The AI/ML algorithm was trained using MRI datasets collected from multiple clinical sites, scanner manufacturers, and imaging protocols to support robustness across the intended use conditions. Training and validation datasets were independent, and no data used for training were included in the validation dataset.
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| MRI2CT | Document Title | Date | Document ID | Page |
| --- | --- | --- | --- | --- |
| | MRI2CT NextMR Spine 510(k) Summary | Sep 1, 2026 | SUB-0001 Revision 03 | 4 of 10 |
## 5. Intended Use / Indications for Use
MRI2CT NextMR Spine is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the 3D bone morphology in MRI images.
It is to be used in the lumbar spine vertebrae (L1-L5) and the sacrum.
MRI2CT NextMR Spine is indicated for use in patients 18 years and older.
MRI2CT NextMR Spine images are not intended to replace CT images in general.
MRI2CT NextMR Spine is not to be used for diagnosis or monitoring of (primary or metastatic) tumors or provide a quantitative bone mineral density diagnosis or bone quality assessment.
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| MRI2CT | Document Title | Date | Document ID | Page |
| --- | --- | --- | --- | --- |
| | MRI2CT NextMR Spine 510(k) Summary | Sep 1, 2026 | SUB-0001 Revision 03 | 5 of 10 |
## 6. Technological Characteristics Compared to Predicate
A comparison of the intended use, indication for use, and technological characteristics of the subject device with the predicate device is presented below. Both devices are software-only image processing systems using deep-learning algorithms to enhance MRI images. Differences in anatomical scope, reconstructed features, and user interface do not raise new questions of safety or effectiveness.
Table 1 Predicate device comparison summary
| Characteristic | Predicate BoneMRI, K233030 | NextMR Spine | Conclusion |
| --- | --- | --- | --- |
| 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 increased contrast with respect to the surrounding soft tissue. | NextMR Spine 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. | Substantially Equivalent Both devices are image processing software intended to enhance MRI images to improve visualization of bony structures. While the predicate also presents the surrounding soft tissue, MRI2CT NextMR Spine is designed to present only the enhanced bony structures. This difference reflects a more focused image presentation and does not alter the intended use or the underlying image enhancement technology. Therefore, it does not raise different questions of safety or effectiveness. |
| Indication for Use | 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. | MRI2CT NextMR Spine is an image processing software that can be used for image enhancement in MRI images. It can be used to visualize the 3D bone morphology in MRI images. It is to be used in the lumbar spine vertebrae (L1-L5) and the sacrum. MRI2CT NextMR Spine is indicated for use in patients 18 years and older. MRI2CT NextMR Spine images are not intended to replace CT images in general. MRI2CT NextMR Spine is not to be used for diagnosis or monitoring of (primary or metastatic) tumors or provide a quantitative bone mineral density diagnosis or bone quality assessment. | Narrower scope |
| Regulation / Product Code | 21 CFR 892.2050 / QIH | 21 CFR 892.2050 / QIH | Identical |
| Device Type | Software-only | Software-only | Identical |
| Algorithm | Locked deep-learning | Locked deep-learning | Identical |
{9}
| MRI2CT | Document Title | Date | Document ID | Page |
| --- | --- | --- | --- | --- |
| | MRI2CT NextMR Spine 510(k) Summary | Sep 1, 2026 | SUB-0001 Revision 03 | 6 of 10 |
| Characteristic | Predicate BoneMRI, K233030 | NextMR Spine | Conclusion |
| --- | --- | --- | --- |
| Anatomical Region | Pelvis & Spine | Lumbar Spine (L1-L5) & the sacrum. | Narrower scope |
| Target Population | ≥12 years | ≥18 years | More conservative |
| Input/Output | MRI DICOM / DICOM | MRI DICOM / DICOM | Identical |
| MRI Acquisition Sequence | Specific gradient echo acquisition sequence | Standard T1-Weighted or T2-weighted | More conservative |
| Performance Matrix | Cortical delineation error, radiodensity deviation and radiodensity contrast | Cortical delineation error, Dice similarity coefficient, radiodensity deviation and radiodensity contrast | The addition of the Dice similarity coefficient as a performance metric further evaluates the accuracy of bone morphology reconstruction and does not raise different questions of safety or effectiveness. |
| User Interface | PACS-based | Secure web-based | Substantially Equivalent The subject device provides a web-based user interface for access to processed DICOM image series, whereas the predicate device utilizes a PACS-based interface. This difference does not raise new questions of safety or effectiveness. Potential risks associated with web-based access (e.g., data transmission security and access control) are mitigated through implementation of cybersecurity controls and software life cycle processes in accordance with IEC 62304 and ISO 14971, including secure authentication and encryption mechanisms. |
| 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 and is a cloud-based, standalone image processing software that does not require any local installation. The software enhances the images, and stores the enhanced images on the server. Enhanced images co-exist with the original images. | Substantially Equivalent No new risks |
{10}
| MRI2CT | Document Title | Date | Document ID | Page |
| --- | --- | --- | --- | --- |
| | MRI2CT NextMR Spine 510(k) Summary | Sep 1, 2026 | SUB-0001 Revision 03 | 7 of 10 |
## 7. Performance Data
The following performance testing was conducted for MRI2CT NextMR Spine:
### Software Verification and Validation (V&V)
Software verification and validation activities were performed in accordance with IEC 62304:2006/Amd1:2015 and FDA's Guidance for Industry and FDA Staff, "Content of Premarket Submissions for Device Software Functions" (June 2023) to confirm that the software meets its specified requirements and intended use.
### Cybersecurity
Cybersecurity testing was conducted and documentation was provided as recommended in FDA's guidance document "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions." (June 25).
### Performance Validation
Performance validation of MRI2CT NextMR Spine was conducted using a retrospective dataset of 164 adult subjects with paired MRI and CT scans in the same anatomical region. Performance was assessed using a predefined quantitative endpoint comparing MRI2CT-generated bone reconstructions to CT-based reference segmentations (ground truth), including mean absolute error (MAE), Dice similarity coefficient, bone tissue radiodensity, and bone tissue radiodensity-contrast. The test dataset consisted of independent data that were not used during algorithm development, training, or tuning. The demographics and performance data, including subgroup analysis of the patient population are described in the table below.
### Quantitative Accuracy Metrics
Performance testing demonstrated reconstruction of three-dimensional (3D) vertebral bone morphology with a mean absolute cortical delineation error of less than 1.0 mm on average. Segmentation performance achieved a Dice Similarity Coefficient (DSC) greater than 0.93, demonstrating a high degree of anatomical agreement with the reference standard. In addition, radiodensity reconstruction performance demonstrated a mean absolute error of less than 86 HU and a radiodensity correlation coefficient greater than 0.75 relative to the reference CT.
{11}
| MRI2CT | Document Title | Date | Document ID | Page |
| --- | --- | --- | --- | --- |
| | MRI2CT NextMR Spine 510(k) Summary | Sep 1, 2026 | SUB-0001 Revision 03 | 8 of 10 |
Table 1 T1-Weighted validation data demographics and performance results.
Statistical testing was with a significance level of \( \alpha = 0.025 \) .
| Subgroup | N (%) | Gender (%) | Age | Data Origin | Cortical Delineation Error (mm) | Radiodensity Error (HU) | Radiodensity Correlation | Dice Coefficient |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Total | 145 | Female 71 (49.0%) | 57 ± 15 | US Israel | < 1 mm | < 86 HU | >0.87 | >0.93 |
| | | Male 74 (51.4%) | 53 ± 17 | | < 1 mm | < 86 HU | | |
| Region | US 85 (58.6%) | Female 43 (29.7%) | 58 ± 14 | US | < 1 mm | < 86 HU | >0.87 | >0.93 |
| | | Male 42 (29.0%) | 51 ± 17 | | | | | |
| | Israel 60 (41.4%) | Female 28 (19.3%) | 54 ± 15 | Israel | < 1 mm | < 86 HU | | |
| | | Male 32 (22.1%) | 57 ± 17 | | | | | |
| BMI | Obese21(14.5%) | Female 8 (5.5%) | 55 ± 13 | US | < 1 mm | < 86 HU | >0.87 | >0.93 |
| | | Male 13 (9.0%) | 54 ± 16 | | | | | |
| | Overweight 32 (22.1%) | Female 13 (9.0%) | 53 ± 12 | | < 1 mm | < 86 HU | | |
| | | Male 19 (13.1%) | 53 ± 17 | | | | | |
| | Normal 20 (13.8%) | Female 14 (9.7%) | 57 ± 16 | | < 1 mm | < 86 HU | | |
| | | Male 6 (4.1%) | 30 ± 12 | | | | | |
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| MRI2CT | Document Title | Date | Document ID | Page |
| --- | --- | --- | --- | --- |
| | MRI2CT NextMR Spine 510(k) Summary | Sep 1, 2026 | SUB-0001 Revision 03 | 9 of 10 |
Table 2 T2-Weighted validation data demographics and performance results. Statistical testing was with a significance level of \( \alpha = 0.025 \) .
| Subgroup | N (%) | Gender (%) | Age | Data Origin | Cortical Delineation Error (mm) | Radiodensity Error (HU) | Radiodensity Correlation | Dice Coefficient |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Total | 144163 | Female 70 (48.6%) | 56 ± 15 | US Israel | < 1 mm | < 86 HU | >0.87 | >0.93 |
| | | Male 74 (51.4%) | 53 ± 17 | | < 1 mm | < 86 HU | | |
| Region | US 84 (58.3%) | Female 42 (29.2%) | 58 ± 14 | US | < 1 mm | < 86 HU | >0.87 | >0.93 |
| | | Male 42 (29.2%) | 51 ± 17 | | | | | |
| | Israel 60 (41.7%) | Female 28 (19.4%) | 54 ± 16 | Israel | < 1 mm | < 86 HU | | |
| | | Male 32 (22.2%) | 57 ± 17 | | | | | |
| BMI | Obese 20 (13.9%) | Female 7 (4.9%) | 52 ± 10 | US | < 1 mm | < 86 HU | >0.87 | >0.93 |
| | | Male 13 (9.0%) | 54 ± 16 | | | | | |
| | Overweight 32 (22.2%) | Female 13 (9.0%) | 53 ± 12 | | < 1 mm | < 86 HU | | |
| | | Male 19 (13.2%) | 53 ± 17 | | | | | |
| | Normal 20 (13.9%) | Female 14 (9.7%) | 57 ± 16 | | < 1 mm | < 86 HU | | |
| | | Male 6 (4.2%) | 30 ± 12 | | | | | |
{13}
| MRI2CT | Document Title | Date | Document ID | Page |
| --- | --- | --- | --- | --- |
| | MRI2CT NextMR Spine 510(k) Summary | Sep 1, 2026 | SUB-0001 Revision 03 | 10 of 10 |
### 8. Substantial Equivalence Conclusion
Performance testing demonstrated that MRI2CT NextMR Spine achieves performance consistent with its intended use, and accurately reconstructs vertebral anatomy with high anatomical agreement with the reference standard. The overall performance demonstrates that MRI2CT NextMR Spine provides equivalent clinical utility for its intended assistive visualization purpose and does not raise new questions of safety or effectiveness.
Therefore, MRI2CT NextMR Spine is substantially equivalent to BoneMRI.
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1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
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Narrow by SaMD (Software as a Medical Device), then narrow by AI/ML (devices with AI).
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Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
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Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
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4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.