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Neurophet SCALE PET is a software for the registration, fusion, display and analysis of medical images from multiple modalities including MRI and PET. The software aids clinician in the assessment and quantification of pathologies from PET Amyloid/FDG scans of the human brain. It enables automatic analysis and visualization of amyloid protein concentration through the calculation of standard uptake volume ratios (SUVR) within target regions of interest and comparison to those within the reference regions.

The software is deployed via medical imaging workplaces and is organized as a series of workflows which are specific to use with radio-tracer and disease combinations.

Neurophet SCALE PET is a standalone software product that automatically calculates standardized uptake value ratios (SUVR) and provides quantified calculation results for quantitative analysis of FDG and Amyloid PET images. The calculation results are intended to aid clinicians in diagnosing patients' pathologies. Furthermore, the function provided to visualize the results of the analysis of the image is designed to help clinicians perform accurate visual interpretation.

Functions and workflow supported by the product are as follows: The user may set specified region as reference region, however, in order to achieve reliable constant count in the reference region, FDA recommends the selection of the pons or cerebellar white matter as reference region for assessment of SUVR in FDG PET imaging.

Because this product complies with the standard DICOM medical imaging protocol, it can be used by being linked with picture archive and communications systems (PACS).

Here's an analysis of the acceptance criteria and study details for the Neurophet SCALE PET device, based on the provided document:

Acceptance Criteria and Device Performance

Study Details

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

The document does not explicitly state the sample size used for the test set in the performance studies. It also does not specify the provenance of the data (e.g., country of origin, retrospective or prospective).

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

The document states that "manual segmentation done by experts" was used for the segmentation accuracy evaluation. However, it does not specify the number of experts involved or their qualifications (e.g., "radiologist with 10 years of experience").

4. Adjudication method for the test set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for establishing the ground truth for the test set. It only mentions "manual segmentation done by experts."

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

No, a multi-reader, multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance was not reported in this document. The studies focus on the standalone performance of the device.

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

Yes, a standalone performance study was done. The Segmentation Accuracy and SUVR Calculation Reliability tests evaluate the algorithm's performance in comparison to expert manual segmentations and conventional PET processing tools, respectively. This assesses the device's capabilities without a human-in-the-loop directly interpreting the results or making a diagnosis.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The ground truth used for:

• Segmentation Accuracy: "Manual segmentation done by experts." This indicates expert consensus-based ground truth.
• SUVR Calculation Reliability: Comparison against "conventional PET processing tools." While not explicitly "ground truth" in the clinical sense, it serves as a reference standard for reliability.

8. The sample size for the training set

The document does not provide any information regarding the sample size used for the training set.

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

The document does not provide any information on how the ground truth for the training set was established.

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