SubtlePET is an image processing software intended for use by radiologists and nuclear medicine physicians for transfer, storage, and noise reduction of fluorodeoxyglucose (FDG), amyloid, 18F-DOPA, 18F-DCFPyL, Ga-68 Dotatate, and Ga-68 PSMA radiotracer PET images.

Device Description

The SubtlePET image processing software reduces noise to increase image quality using a deep neural network-based algorithm.

The software employs a convolutional network-based method in a pixel's neighborhood to generate the value for each pixel. Using a residual learning approach, the software predicts the noise components and structural components. The software separates these components, which enhances the structure while simultaneously reducing the noise.

The workflow of the product can be easily adapted to existing radiology departmental workflow. The product acts as a DICOM node that receives DICOM 3.0 digital medical image data from the modality or another DICOM source, processes the data and then forwards the enhanced study to the selected destination. This destination can be any DICOM node, typically either the PACS system or a specific workstation.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study information for SubtlePET, based on the provided document:

Acceptance Criteria and Device Performance

Acceptance Criteria Objective	Reported Device Performance
Noise reduction to increase image quality in PET scans.	Significant average increase in quantitative metrics for all cases, demonstrating that the software reduced noise in PET scans.

Study Information

2. Sample size used for the test set and data provenance:

The document states that the noise reduction bench test utilized "representative cases of human data." However, it does not specify the exact sample size used for this test set.
The data provenance is described as "human data already gathered under the auspices of IRB-approved clinical protocols." This indicates the data is retrospective and was collected according to ethical guidelines. The country of origin is not explicitly stated.

3. Number of experts used to establish the ground truth for the test set and qualifications of those experts:

The document does not provide information regarding the number of experts used or their qualifications for establishing ground truth specifically for the test set.

4. Adjudication method for the test set:

The document does not specify an adjudication method used for the test set.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or any effect size related to human reader improvement with/without AI assistance. The performance data focuses on quantitative metrics of noise reduction.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

Yes, a standalone performance assessment was conducted. The "Noise reduction bench test utilizing representative cases of human data" and the reported "significant average increase in quantitative metrics" describe the algorithm's performance independent of a human reader in a diagnostic workflow.

7. The type of ground truth used:

The document implies a "reference standard" or "gold standard" for noise reduction based on the quantitative metrics. However, it does not explicitly state what this ground truth was (e.g., a "true" noise-free image, or a statistically derived reference). It focuses on the algorithm's ability to reduce noise relative to the input image, rather than diagnosing a condition against a pathology report.

8. The sample size for the training set:

The document does not specify the sample size used for the training set of the deep neural network.

9. How the ground truth for the training set was established:

The document does not explicitly describe how ground truth was established for the training set. It mentions the software uses a "deep neural network-based algorithm" that employs a "convolutional network-based method" and a "residual learning approach" to predict noise and structural components. This suggests the training would involve pairs of noisy and "cleaner" or target images, but the exact method for generating or establishing the "cleaner" ground truth is not detailed.

Summary

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September 28, 2021

Image /page/0/Picture/1 description: The image contains 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.

Subtle Medical, Inc. % Jared Seehafer Regulatory Consultant Enzyme Corporation 611 Gateway Blvd #120 SOUTH SAN FRANCISCO CA 94080

Re: K211964

Trade/Device Name: SubtlePET Regulation Number: 21 CFR 892.1200 Regulation Name: Emission computed tomography system Regulatory Class: Class II Product Code: KPS, LLZ Dated: August 31, 2021 Received: September 2, 2021

Dear Jared 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 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

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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 (QS) 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,

Michael D. O'Hara

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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Indications for Use

510(k) Number (if known) K211964

Device Name SubtlePET

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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Table 1. Subject Device Overview.
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:	23-SEPT-2021
Device Proprietary Name:	SubtlePET
Model Number:	V 2.0.0
Common Name:	SubtlePET
Regulation Number:	21 CFR 892.1200
Regulation Name:	Emission computed tomography system
Product Codes:	KPS, LLZ
Device Class:	Class II
Predicate Device	Trade name: SubtlePETManufacturer: Subtle Medical, Inc.Regulation Number: 21 CFR 892.1200Regulation Name: Emission computed tomographysystemDevice Class: Class IIProduct Codes: KPS, LLZ510(k) Number: K182336510(k) Clearance Date: November 30, 2018

Table 1. Subject Device Overview.

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1. Device Description

The SubtlePET image processing software reduces noise to increase image quality using a deep neural network-based algorithm.

2. Indications for Use

Table 2 compares the indications for use of the predicate and subject device.

Predicate Device	Subject Device	Differences
SubtlePET is an imageprocessing softwareintended for use byradiologists and nuclearmedicine physicians fortransfer, storage, and noisereduction offluorodeoxyglucose (FDG)and amyloid PET images(including PET/CT andPET/MRI)."	SubtlePET is an imageprocessing softwareintended for use byradiologists and nuclearmedicine physicians fortransfer, storage, andnoise reduction offluorodeoxyglucose(FDG), amyloid, 18F-DOPA, 18F-DCFPyL, Ga-68 Dotatate, and Ga-68PSMA radiotracer PETimages.	Substantially similar. Thesubject device IFUremoves reference toPET/CT and PET/MRIimages as the specificmodels for those imageshave been removed, whilelisting additional tracers toreflect the update of themain machine learningmodel for PET images toaccommodate thosetracers.

Table 2. Indications of Use Comparison.

3. Technological Characteristics

Table 3 compares the technological characteristics of the predicate and subject device.

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Topic	Predicate Device	Subject Device
PhysicalCharacteristics	Software package that operates onoff-the-shelf hardware	Same
Computer	Linux Compatible	Same
DICOMStandardCompliance	The software processes DICOMcompliant image data	Same
OperatingSystem	Linux	Same
Modalities	PET	Same
User Interface	None	Same
ImageEnhancementAlgorithmDescription	The software employs aconvolutional neural network-based method in a pixel'sneighborhood to generate thevalue for each pixel.Using a residual learningapproach, the software predictsthe noise components andstructural components. Thesoftware separates thesecomponents, which enhances thestructure while simultaneouslyreducing the noise.	Same
Radiotracerssupported	fluorodeoxyglucose (FDG),amyloid	fluorodeoxyglucose (FDG),amyloid, 18F-DOPA,18F-DCFPyL, Ga-68 Dotatate,Ga-68 PSMA
Deep learningmodel(s)	PET, PET/CT, PET/MRI	PET

Table 3. Summary of Technological Characteristics Comparison.

4. Performance Data

Subtle Medical conducted the following performance testing:

Software verification and validation testing ●
. Noise reduction bench test utilizing representative cases of human data already gathered under the auspices of IRB-approved clinical protocols. The study showed a significant average increase in quantitative metrics for all cases demonstrating that the software reduced noise in PET scans.

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Based upon the results of this testing, the SubtlePET performance was determined to be substantially equivalent to the predicate device.

5. Substantial Equivalence Conclusion

This 510(k) is being filed as a device modification to a currently legally marketed device, SubtlePET. The intended use remains identical, the indications for use are substantially similar, reflecting an update to the SubtlePET device to remove machine learning models for PET/CT and PET/MRI images, while updating the main machine learning model for PET images to accommodate additional tracers. This modification to SubtlePET is as safe and effective as the predicate, and does not raise different questions of safety and effectiveness.

Regulation Number and Section

§ 892.1200 Emission computed tomography system.

(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.