AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K191688 · Sep 16, 2019
SubtleMR
Subtle Medical, Inc.
Retrospective clinical MRI datasets
Retrospective clinical MRI images were used to evaluate the performance of the SubtleMR software in reducing noise and increasing image sharpness compared to original clinical images.
Retrospective clinical performance study; Retrospective analysis of clinical MRI images
Head, spine, neck, and knee MRI clinical cases
Original (pre-enhancement) clinical MRI images
Signal-to-noise ratio (SNR) improvement; visibility of small structures; thickness of anatomic structure; sharpness of structure boundaries
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
MRI Noise Reduction
CNN with cascaded filter banks
SNR improvement >= 5% and visibility of small structures difference <= 0.5 Likert scale points
Passed
—
—
Retrospective clinical data study
—
MRI Image Sharpness
CNN with cascaded filter banks
Anatomic structure thickness and boundary sharpness improved in >= 90% of test datasets
Passed
—
—
Retrospective clinical data study
—
Indications for Use
SubtleMR is an image processing software that can be used for image enhancement in MRI images. It can be used to reduce image noise for head, spine, neck and knee MRI, or increase image sharpness for non-contrast enhanced head MRI.
Device Story
SubtleMR is SaMD that processes standard-of-care MRI images (1.2T, 1.5T, 3T) to improve quality. It uses a convolutional neural network-based algorithm to perform noise reduction or sharpness enhancement. The software operates on DICOM files on a file system; it has no user interface. Radiologists in hospitals or clinics use the output, which consists of enhanced images stored alongside original images on PACS workstations. The device benefits patients by providing higher-quality images for clinical interpretation. The enhancement choice is triggered via DICOM Series Description, command line, or environment variable.
Clinical Evidence
Performance testing included software verification/validation and a retrospective clinical study. Noise reduction test: SNR improved by ≥5% in ROIs; small structure visibility change ≤0.5 Likert scale points. Sharpness increase test: anatomic structure thickness and boundary sharpness improved in ≥90% of test datasets. Results demonstrate substantial equivalence to the predicate.
Technological Characteristics
SaMD; convolutional neural network-based filtering; fixed nonlinear filter; Linux-compatible; processes DICOM-compliant image data; no user interface; operates on standard-of-care MRI images (1.2T, 1.5T, 3T).
Indications for Use
Indicated for image enhancement in MRI images, specifically noise reduction for head, spine, neck, and knee MRI, or sharpness increase for non-contrast enhanced head MRI.
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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September 16, 2019
Subtle Medical, Inc. % Mr. Jared Seehafer Regulatory Consultant Enzyme Corporation 360 Langton Street, Suite 100 SAN FRANCISCO CA 94103
Re: K191688
Trade/Device Name: SubtleMR Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: July 15, 2019 Received: July 17, 2019
Dear Mr. Seehafer:
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 (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 located 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.
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
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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 803) for devices or postmarketing safety reporting (21 CFR 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 (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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 (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,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Form Approved: OMB No. 0910-0120
Expiration Date: 06/30/2020
See PRA Statement below.
#### DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
### Indications for Use
510(k) Number (if known)
### K191688
Device Name SubtleMR
Indications for Use (Describe)
SubtleMR is an image processing software that can be used for image enhancement in MRI images. It can be used to reduce image noise for head, spine, neck and knee MRI, or increase image sharpness for non-contrast enhanced head MRI.
Type of Use (Select one or both, as applicable)
> Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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FORM FDA 3881 (7/17)
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# 5 510(k) Summary
| 510(k) - SubtleMR |
|-------------------|
| |
K191688
| Submitter's Name: | Subtle Medical, Inc. |
|--------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Address: | 883 Santa Cruz Ave, Suite 205<br>Menlo Park, CA 94025 |
| Contact Person: | Jared Seehafer |
| Title: | Regulatory Consultant |
| Telephone Number: | 415-857-9554 |
| Fax Number: | 415-367-1279 |
| Email: | jared@enzyme.com |
| Date Summary Prepared: | 24-JUN-2019 |
| Device Proprietary Name: | SubtleMR |
| Model Number: | V 1.0.0 |
| Common Name: | SubtleMR |
| Regulation Number: | 21 CFR 892.2050 |
| Regulation Name: | System, Image Processing, Radiological |
| Product Code: | LLZ |
| Device Class: | Class II |
| Predicate Device | Trade name: ZOOM<br>Manufacturer: Zetta Medical Technologies, LLC.<br>1313 Ensell Road<br>Lake Zurich, IL 60047<br>Regulation Number: 21 CFR 892.2050<br>Regulation Name: System, Image Processing,<br>Radiological<br>Device Class: Class II<br>Product Code: LLZ<br>510(k) Number: K172768<br>510(k) Clearance Date: April 24, 2018 |
## Table 5-1. Subject Device Overview.
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## 5.1 Device Description
SubtleMR is Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. As it only processes images for the end user, the device has no user interface. It is intended to be used by radiologists in an imaging center, clinic, or hospital. The software can be used with MR images acquired as part of MRI exams on 1.2 Tesla, 1.5 Tesla or 3 Tesla scanners. The device's inputs are standard of care MRI images. The outputs are images with enhanced image quality.
The software uses a convolutional network-based algorithm to improve image quality by reducing noise or increasing the image sharpness. The algorithm's specific parameters vary depending on the choice of image enhancement: noise reduction or image sharpness increase, while the network designs are similar. For each choice, there is a fixed set of parameters and the algorithm is working as a fixed nonlinear filter. The choice of image enhancement is made by the end user via the DICOM Series Description, command line argument, or environment variable.
## 5.2 Indications for Use
SubtleMR is an image processing software that can be used for image enhancement in MRI images. It can be used to reduce image noise for head, spine, neck and knee MRI, or increase image sharpness for non-contrast enhanced head MRI.
## 5.3 Summary of Technological Characteristics Comparison
Table 5-2 shows the similarities and differences between the technological characteristics of the two products. The key difference is software algorithm. Testing demonstrates that the differences do not raise new questions of safety or effectiveness.
| Topic | Predicate Device | Subject Device |
|---------------------------------|-----------------------------------------------------------------|-----------------------------------------------------------------------|
| Physical<br>Characteristics | Software package that operates on<br>off-the-shelf hardware | Same |
| Computer | PC Compatible | Linux Compatible |
| DICOM<br>Standard<br>Compliance | The software processes DICOM<br>compliant image data | Same |
| Operating<br>System | Windows | Linux |
| Modalities | MRI | Same |
| User Interface | The software is designed for use<br>on a radiology workstation. | None - enhanced images are<br>viewed on existing PACS<br>workstations |
Table 5-2. Summary of Technological Characteristics Comparison.
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| Topic | Predicate Device | Subject Device |
|--------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Image<br>Enhancement<br>Algorithm<br>Description | ZOOM image enhancement<br>software implements a noise<br>reduction algorithm using<br>wavelets and image guided<br>filtering. Original images are<br>decomposed into different<br>wavelet sub bands and noise in<br>each band is a soft threshold. De-<br>noised images are reconstructed<br>from soft thresholded images<br>using inverse wavelet transform. | SubtleMR software implements<br>an image enhancement<br>algorithm using convolutional<br>neural network based filtering.<br>Original images are enhanced<br>by running through a cascade of<br>filter banks, where thresholding<br>and scaling operations are<br>applied. Separate neural<br>network based filters are<br>obtained for noise reduction<br>and sharpness increase. The<br>parameters of the filters were<br>obtained through an image-<br>guided optimization process. |
| Workflow | The software, which is installed<br>on a remote computer, receives<br>DICOM images from MRI host<br>computer, automatically<br>processes the received images and<br>sends the enhanced images to a<br>PACS server. Enhanced images<br>exist in conjunction to the original<br>images. | The software operates on<br>DICOM files on the file system,<br>enhances the images, and stores<br>the enhanced images on the file<br>system. The receipt of original<br>DICOM image files and<br>delivery of enhanced images as<br>DICOM files depends on other<br>software systems. Enhanced<br>images co-exist with the<br>original images. |
### 5.3 Performance Data
Subtle Medical conducted the following performance testing:
- Software verification and validation testing ●
- . Study that utilized retrospective clinical data to demonstrate the software enhanced image quality in MR images via a reduction of noise or an increase of image sharpness.
The main performance study, utilizing retrospective clinical data, was divided into two tests.
For the noise reduction performance test, acceptance criteria were that signal-to-noise ratio (SNR) of a selected region of interest (ROI) in each test dataset is on average improved by greater than or equal to 5% after SubtleMR enhancement compared to the original images,
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and (ii) the visibility of small structures in the test datasets before and after SubtleMR is on average less than or equal to 0.5 Likert scale points. This test passed.
For the sharpness increase performance test, acceptance criteria were that the thickness of anatomic structure and the sharpness of structure boundaries are improved after SubtleMR enhancement in at least 90% of the test datasets. This test passed.
Based upon the results of this testing, the SubtleMR performance was determined to be substantially equivalent to the predicate device.
## 5.4 Substantial Equivalence Conclusion
SubtleMR is an image enhancement software which has similar intended use and indications for use statement as the predicate device. The two devices have similar technological characteristics: both algorithms use image based nonlinear filtering and reconstruction, and both methods have optimized parameters to ensure robustness of the algorithm. This 510(k) submission includes information on the SubtleMR technological characteristics, as well as performance data and verification and validation activities demonstrating that SubtleMR is as safe and effective as the predicate, and does not raise different questions of safety and effectiveness.
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
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.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
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.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
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.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
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.