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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.

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.

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

Acceptance Criteria and Device Performance

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.

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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:

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.

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