(96 days)
Not Found
Yes
The device description explicitly mentions using a "deep neural network-based algorithm" and a "convolutional network-based method," which are forms of deep learning, a subset of machine learning and artificial intelligence.
No.
The device is described as image processing software that reduces noise in PET images to increase image quality. Its intended use is by radiologists and nuclear medicine physicians for transfer, storage, and noise reduction of radiotracer PET images, not for treating a disease or condition.
No
This device is image processing software designed to improve image quality by reducing noise. It does not perform diagnosis or provide diagnostic output; its output is enhanced images for use by physicians.
Yes
The device is explicitly described as "image processing software" and its function is solely based on algorithms applied to existing image data. There is no mention of any accompanying hardware component that is part of the device itself.
Based on the provided information, this device is NOT an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The intended use is for image processing software used by radiologists and nuclear medicine physicians for tasks like transfer, storage, and noise reduction of PET images. This is focused on enhancing the quality and management of medical images, not on performing tests on biological samples to diagnose or monitor a medical condition.
- Device Description: The description details how the software processes image data using algorithms to reduce noise and improve image quality. It acts as a DICOM node to handle image data flow. This is consistent with medical image processing software, not an IVD.
- Lack of Biological Sample Analysis: IVDs are designed to perform tests on biological samples (like blood, urine, tissue, etc.) to provide information about a patient's health status. This device does not interact with or analyze biological samples.
- Focus on Image Enhancement: The core function described is noise reduction and image quality improvement, which are typical functions of medical image processing software.
In summary, the device's function and intended use are centered around the manipulation and enhancement of medical images, not the analysis of biological samples for diagnostic purposes. Therefore, it does not fit the definition of an In Vitro Diagnostic device.
No
The provided text does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The 'Control Plan Authorized (PCCP) and relevant text' section is marked 'Not Found'.
Intended Use / Indications for Use
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 Dotate, and Ga-68 PSMA radiotracer PET images.
Product codes
KPS, LLZ
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
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.
Input Imaging Modality
PET
Anatomical Site
Not Found
Indicated Patient Age Range
Not Found
Intended User / Care Setting
radiologists and nuclear medicine physicians
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
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.
Based upon the results of this testing, the SubtlePET performance was determined to be substantially equivalent to the predicate device.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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.
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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
1
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)
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 Dotate, and Ga-68 PSMA radiotracer PET images.
| 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 205 |
| 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: | 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: SubtlePET |
| Manufacturer: Subtle Medical, Inc. | |
| Regulation Number: 21 CFR 892.1200 | |
| Regulation Name: Emission computed tomography | |
| system | |
| Device Class: Class II | |
| Product Codes: KPS, LLZ | |
| 510(k) Number: K182336 | |
| 510(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.
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.
2. Indications for Use
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.
Table 2 compares the indications for use of the predicate and subject device.
| Predicate Device | Subject Device | Differences |
|---|---|---|
| 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)." | 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. | Substantially similar. The | |
| subject device IFU | ||
| removes reference to | ||
| PET/CT and PET/MRI | ||
| images as the specific | ||
| models for those images | ||
| have been removed, while | ||
| listing additional tracers to | ||
| reflect the update of the | ||
| main machine learning | ||
| model for PET images to | ||
| accommodate those | ||
| tracers. |
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 |
|---|---|---|
| Physical | ||
| Characteristics | Software package that operates on | |
| off-the-shelf hardware | Same | |
| Computer | Linux Compatible | Same |
| DICOM | ||
| Standard | ||
| Compliance | The software processes DICOM | |
| compliant image data | Same | |
| Operating | ||
| System | Linux | Same |
| Modalities | PET | Same |
| User Interface | None | Same |
| Image | ||
| Enhancement | ||
| Algorithm | ||
| Description | 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. | Same | |
| Radiotracers | ||
| supported | fluorodeoxyglucose (FDG), | |
| amyloid | fluorodeoxyglucose (FDG), | |
| amyloid, 18F-DOPA, | ||
| 18F-DCFPyL, Ga-68 Dotatate, | ||
| Ga-68 PSMA | ||
| Deep learning | ||
| model(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.