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 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.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for SubtleMR, based on the provided FDA 510(k) summary:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are divided into two main performance tests: noise reduction and sharpness increase.

Performance Metric	Acceptance Criteria	Reported Device Performance
Noise Reduction Test
Signal-to-Noise Ratio (SNR) Improvement	SNR of a selected Region of Interest (ROI) in each test dataset is on average improved by ≥ 5% after SubtleMR enhancement compared to the original images.	The study passed this criterion. (Specific average improvement percentage is not detailed in the provided text, just that it passed).
Visibility of Small Structures	The visibility of small structures in the test datasets before and after SubtleMR is on average ≤ 0.5 Likert scale points (implying minimal or no degradation, or slight improvement in perception).	The study passed this criterion. (Specific average Likert scale change is not detailed in the provided text, just that it passed).
Sharpness Increase Test
Anatomical Structure Thickness & Boundary Sharpness Improvement	The thickness of anatomic structure and the sharpness of structure boundaries are improved after SubtleMR enhancement in at least 90% of the test datasets.	The study passed this criterion. (Specific percentage of datasets improved is not detailed, just that it passed and met the "at least 90%" threshold).

2. Sample Size Used for the Test Set and Data Provenance

The exact sample size for the test set is not explicitly stated in the provided document. It refers to "each test dataset" for the noise reduction test and "at least 90% of the test datasets" for the sharpness increase test, indicating multiple datasets were used.

The data provenance is stated as retrospective clinical data. The country of origin is not specified.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Experts

The document does not specify the number of experts used or their qualifications for establishing the ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1) for the test set. The evaluation seems to have been based on quantitative metrics (SNR) and a Likert scale assessment, but the process of aggregation or reconciliation if multiple readers were involved is not described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study to assess how much human readers improve with AI vs. without AI assistance. The performance tests described focus on quantitative image quality metrics (SNR, sharpness) and a perceptual assessment of small structures, not a reader study of diagnostic accuracy or efficiency.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the described performance tests appear to be standalone (algorithm only) evaluations. The metrics (SNR, Likert scale for structure visibility, and sharpness/thickness improvement percentages) directly assess the output of the algorithm on the images, rather than measuring reader performance with and without the algorithm. The device itself is described as having "no user interface," further suggesting a standalone processing function.

7. The Type of Ground Truth Used

The ground truth for the noise reduction test appears to be derived from a quantitative measurement (SNR) and a perceptual assessment (Likert scale for small structures). For the sharpness increase test, it was based on assessing the improvement in thickness of anatomic structures and sharpness of structure boundaries. These are essentially expert-defined metrics or assessments applied to the processed images, rather than external pathology or outcomes data.

8. The Sample Size for the Training Set

The document does not provide the sample size for the training set. It mentions that the algorithm uses a "convolutional network-based algorithm" whose "parameters... were obtained through an image-guided optimization process," implying a training phase, but the details of the training data are not included in this summary.

9. How the Ground Truth for the Training Set Was Established

The document does not explain how the ground truth for the training set was established. It only states that the "parameters of the filters were obtained through an image-guided optimization process," which is vague regarding the ground truth data used for this optimization. For image enhancement tasks, ground truth often involves pairs of original and "ideal" or "target" enhanced images, or noise-free versions of images, but this is not detailed here.

Summary

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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)

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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5 510(k) Summary

510(k) - SubtleMR

K191688

Submitter's Name:	Subtle Medical, Inc.
Address:	883 Santa Cruz Ave, Suite 205Menlo 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: ZOOMManufacturer: Zetta Medical Technologies, LLC.1313 Ensell RoadLake Zurich, IL 60047Regulation Number: 21 CFR 892.2050Regulation Name: System, Image Processing,RadiologicalDevice Class: Class IIProduct Code: LLZ510(k) Number: K172768510(k) Clearance Date: April 24, 2018

Table 5-1. Subject Device Overview.

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5.1 Device Description

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

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
PhysicalCharacteristics	Software package that operates onoff-the-shelf hardware	Same
Computer	PC Compatible	Linux Compatible
DICOMStandardCompliance	The software processes DICOMcompliant image data	Same
OperatingSystem	Windows	Linux
Modalities	MRI	Same
User Interface	The software is designed for useon a radiology workstation.	None - enhanced images areviewed on existing PACSworkstations

Table 5-2. Summary of Technological Characteristics Comparison.

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Topic	Predicate Device	Subject Device
ImageEnhancementAlgorithmDescription	ZOOM image enhancementsoftware implements a noisereduction algorithm usingwavelets and image guidedfiltering. Original images aredecomposed into differentwavelet sub bands and noise ineach band is a soft threshold. De-noised images are reconstructedfrom soft thresholded imagesusing inverse wavelet transform.	SubtleMR software implementsan image enhancementalgorithm using convolutionalneural network based filtering.Original images are enhancedby running through a cascade offilter banks, where thresholdingand scaling operations areapplied. Separate neuralnetwork based filters areobtained for noise reductionand sharpness increase. Theparameters of the filters wereobtained through an image-guided optimization process.
Workflow	The software, which is installedon a remote computer, receivesDICOM images from MRI hostcomputer, automaticallyprocesses the received images andsends the enhanced images to aPACS server. Enhanced imagesexist in conjunction to the originalimages.	The software operates onDICOM files on the file system,enhances the images, and storesthe enhanced images on the filesystem. The receipt of originalDICOM image files anddelivery of enhanced images asDICOM files depends on othersoftware systems. Enhancedimages co-exist with theoriginal 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.

Regulation Number and Section

§ 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).