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Veuron-Brain-pAb3 is software for the registration, fusion, display, and analysis of medical images from multiple modalities including MRI and PET. The software aids clinicians in the assessment and quantification of pathologies from PET Amyloid scans of the human brain. It enables anatomic analysis and visualization of amyloid protein concentration through the calculation of standard uptake volume ratio (SUVR) within target reqions 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.

The Veuron-Brain-pAb3 is a standalone software for quantitative analysis of the PET amyloid by automatically calculating the "Standardized Uptake Value Ratio (SUVR)". The calculated result is only used as a reference to support the accuracy of the medical professional's diagnosis of dementia in patients. It also helps with accurate visual interpretation through visualization functions. Various PET amyloid images can be processed by using diverse options provided for users to choose in the image process.

The provided text describes the acceptance criteria and the study that proves the device meets those criteria for the Veuron-Brain-pAb3, a medical imaging software.

1. A table of acceptance criteria and the reported device performance:

The document doesn't provide explicit acceptance criteria in a quantitative format (e.g., minimum accuracy percentages, SUVR ranges) for the Veuron-Brain-pAb3. Instead, it states that "Software verification and validation was performed to demonstrate the new functions perform as intended." and "The testing results support that all the system requirements have met their acceptance criteria and are adequate for its intended use."

However, the key functions that define the device's performance, as outlined in the "Summary of Technological Characteristics" and "Device Description," are:

• Automatic calculation of Standardized Uptake Value Ratio (SUVR) within target regions of interest and comparison to reference regions.
• Anatomic analysis and visualization of amyloid protein concentration.
• Registration, fusion, and display of medical images (MRI and PET).
• Accurate visual interpretation through visualization functions.

The document implicitly states that these functions perform as intended, which serves as the "reported device performance" meeting the unspecified acceptance criteria.

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. However, it mentions under 'Segmentation Algorithm' that the CNN model was "trained [on] 3D brain MR images were collected from one domestic institution." This suggests the data used for testing (and training) was from a single domestic institution. It doesn't specify if the data was retrospective or prospective, but given it's part of a model training and validation process, it's highly likely to be retrospective.

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

The document does not specify the number or qualifications of experts used to establish the ground truth for the test set. The ground truth for the segmentation algorithm appears to be implicitly derived from the "3D brain MR images collected from one domestic institution" used for training the CNN model, meaning the "truths" would be the labeled segmentations used to train the model. Given the device's function is quantitative analysis (SUVR calculation), the ground truth for the SUVR calculation itself would be based on the established methodology of SUVR calculation rather than expert annotation for each case.

4. Adjudication method for the test set:

The document does not describe any adjudication method for the test set.

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. The document explicitly states: "No clinical testing was conducted." Therefore, an MRMC comparative effectiveness study was not performed.

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

Yes, implicitly. The device is described as "standalone software for quantitative analysis of the PET amyloid by automatically calculating the 'Standardized Uptake Value Ratio (SUVR)'." The "Non-Clinical Performance Testing" section mentions "Software verification and validation was performed to demonstrate the new functions perform as intended," which would involve evaluating the algorithm's performance in isolation. While the results are not quantitatively detailed, the device's core function is an automated calculation, suggesting standalone performance was assessed.

7. The type of ground truth used:

The type of ground truth for the core SUVR calculation is methodology-based (the calculation itself is a defined process). For the segmentation algorithm, the ground truth would be expert-annotated segmentations of brain MR images, used for training the CNN model.

8. The sample size for the training set:

The document does not specify the exact sample size for the training set, only stating that the CNN model for segmentation was "trained [on] 3D brain MR images were collected from one domestic institution."

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

The ground truth for the CNN model used for segmentation was established through the collection of "3D brain MR images were collected from one domestic institution" that were used to train the model. This implies that these images likely came with pre-existing or expert-derived segmentations necessary for supervised learning. For the SUVR calculation itself, the ground truth is inherently defined by the mathematical formula and anatomical regions used in its computation.

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