(94 days)
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).
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.
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.
§ 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.