SubtlePET is an image processing software intended for use by radiologists and nuclear medicine physicians for transfer, storage, and noise reduction of fluorodeoxyglucose (FDG) and amyloid PET images (including PET/CT and PET/MRI).

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 neural 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 FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state quantitative acceptance criteria or a direct comparison to a specified performace target in a table format. However, it implicitly states the objective is to demonstrate noise reduction and substantial equivalence.

Based on the provided text, the primary stated performance outcome is:

Acceptance Criteria (Implicit)	Reported Device Performance
Noise Reduction: Improve image quality by reducing noise in PET scans.	"The study showed a significant average increase in quantitative metrics for all cases demonstrating that the software reduced noise in PET scans."
Substantial Equivalence: Demonstrate safety and effectiveness comparable to the predicate device.	"Based upon the results of this testing, it was determined the SubtlePET performance was substantially equivalent to the predicate device."

2. Sample Size and Data Provenance for the Test Set

Sample Size for Test Set: The document mentions "representative cases of human data." It does not specify the exact number of cases or scans used in the noise reduction bench test.
Data Provenance: "human data already gathered under the auspices of IRB-approved clinical protocols." This implies the data were retrospective and obtained from human subjects under ethical review. The country of origin is not specified.

3. Number of Experts and Qualifications for Ground Truth

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

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for the test set. The noise reduction bench test appears to be based on quantitative metrics.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was done. There is no mention of human readers evaluating images with and without AI assistance, nor any effect size reported.

6. Standalone (Algorithm Only) Performance

Yes, a standalone (algorithm only) performance assessment was done. The "Noise reduction bench test" evaluating "quantitative metrics" is an example of an algorithm-only performance assessment, as it focuses on the software's ability to reduce noise based on these metrics, independent of human interpretation.

7. Type of Ground Truth Used

The type of ground truth used for the noise reduction bench test appears to be quantitative metrics for noise reduction, rather than expert consensus, pathology, or outcomes data. The document states "significant average increase in quantitative metrics."

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It only describes the algorithm's methodology (deep neural network, convolutional neural network) which implies a training phase, but no details about the data used for training.

9. How Ground Truth for the Training Set Was Established

The document does not specify how ground truth for the training set was established. Given the description of the algorithm (predicting noise and structural components), the ground truth for training would likely involve pairs of noisy and "clean" or reference images, or labels indicating noise characteristics, but this is not detailed in the provided text.

Summary

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Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA) along with the Department of Health & Human Services. The FDA logo features a blue square with the letters 'FDA' in white, followed by the words 'U.S. FOOD & DRUG ADMINISTRATION' in blue. To the left of the FDA logo is the Department of Health & Human Services seal, which includes an emblem with a caduceus-like symbol and the text 'DEPARTMENT OF HEALTH & HUMAN SERVICES-USA' arranged around it.

November 30, 2018

Subtle Medical, Inc. % Ms. Terese Bogucki Regulatory Consultant Decus Biomedical Inc. 2342 Shattuck Ave #333 BERKELEY CA 94704

Re: K182336

Trade/Device Name: SubtlePET Regulation Number: 21 CFR 892.2050 Regulation Name: Picture Archiving and Communications System Regulatory Class: Class II Product Code: LLZ Dated: August 27, 2018 Received: August 28, 2018

Dear Ms. Bogucki:

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 mav, 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 avare 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 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/CombinationProducts/GuidanceRegulatoryInformation/ucm597488.html; 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 http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/) and CDRH Learn (http://www.fda.gov/Training/CDRHLearn). 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 (http://www.fda.gov/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

Robert A. Ochs, Ph.D. Director Division of Radiological Health Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health

Enclosure

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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration

Indications for Use

Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2020 See PRA Statement below.

510(k) Number (if known)

K182336

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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5 510(k) Summary

Table 5-1. Subject Device Overview.
Submitter's Name:	Subtle Medical, Inc.
Address:	880 Santa Cruz Ave, Suite 200Menlo Park, CA 94025
Contact Person:	Terese Bogucki
Title:	Regulatory Consultant
Telephone Number:	650-488-7799
Fax Number:	650-227-2264
Email:	terri@decusbiomedical.com
Date Summary Prepared:	5-NOV-2018
Device Proprietary Name:	SubtlePET
Model Number:	V 1.0.0
Common Name:	SubtlePET
Regulation Number:	21 CFR 892.2050
Regulation Name:	Picture archiving and communications system
Primary Product Code:	LLZ
Secondary Product Code:	KPS
Device Class:	Class II
Predicate Device:	Trade name: Sapheneia ClarityManufacturer: Sapheneia Commercial Products ABAddress: Teknikringen 8SE-583 30 Linkoping, SwedenRegulation Number: 21 CFR 892.2050Regulation Name: Picture Archiving andCommunications SystemDevice Class: Class IIProduct Code: LLZ510(k) Number: K063391510(k) Clearance Date: April 26, 2007

Table 5-1. Subiect Device Overview

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5.1 Device Description

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

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 the imaging modality type. 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	Software package that operateson a virtual machine (VM)
Computer	PC Compatible	Virtual machine host-compatible system
ImageProcessingEnhancementLocation	Onsite on the desktop computerserver	Onsite on the facility VMand/or offsite on the cloud VM,depending on the site'sconfiguration
DICOMStandardCompliance	The software processes DICOMcompliant image data	Same
OperatingSystem	Windows	CentOS 7 Linux

Table 5-2. Summary of Technological Characteristics Comparison

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Topic	Predicate Device	Subject Device
Modalities	Multi-modality	Multi-modality; specificallyprocesses PET, PET/CT andPET/MR images
User Interface	The software is designed for useon a radiology workstation. It isunknown whether there is a userinterface.	None - enhanced images areviewed on existing PACSworkstations
Protocols	Custom low dose protocols	Standard scanner protocols
ImageEnhancementAlgorithmDescription	Sapheneia Clarity™ employs asophisticated statistical analysisof the image structure in theneighborhood of each pixel.Using robust estimation methodsthe dominant structures areseparated from the embeddingnoise. Once the structure has beendetermined, it is possible tostrengthen the interesting partswhile simultaneously reducing thenoise.	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 enhancesthe structure whilesimultaneously reducing thenoise.
ImageAcquisition	The acquisition remains the same,i.e. the image processing can begenerated from multiplemodalities and with predefined orspecific acquisition protocolsettings.	The acquisition remains thesame.

5.4 Performance Data

Subtle Medical conducted the following non-clinical performance tests:

. Design traceability confirming all requirement tracing is complete from design inputs and verification/validation and that all risk controls are implemented
Design verification testing which included confirming all labeling complies . with 21CFR801 and all software requirements work as expected
Design validation testing simulated intended use to confirm that the end-to-. end functionality of the SubtlePET DICOM Dispatcher in conjunction with the SubtlePET algorithm meets the design requirements

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

5.5 Substantial Equivalence Conclusion

SubtlePET is an image enhancement software which has similar intended use and indications for use statement as the predicate device. The main difference in indications for use is that the predicate and subject devices apply enhancement to different multimodality image types. The two devices have similar technological characteristics: both algorithms are fixed image-domain nonlinear filtering that uses neighborhood information, and both methods have optimized parameters to ensure robustness and adaption to variable structures, tissues, noises and scales. This 510(k) submission includes information on the SubtlePET technological characteristics, as well as performance data and verification and validation activities demonstrating that 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.