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A nuclear medicine image display and processing application suite that provides software applications used to process, analyze, and display medical images/data. The results obtained may be used as a tool, by a nuclear physician, in determining the diagnosis of patient disease conditions in various organs, tissues, and other anatomical structures. The data processed may be derived from any nuclear medicine gamma camera. The NM Application Suite should only be operated by qualified healthcare professionals trained in the use of nuclear medicine equipment.

The NM Application Suite is a Windows®-based Nuclear Medicine suite of image display and processing applications for the Nuclear Medicine market segment. The software package is deployable on hardware platforms, which meet the minimum requirements needed to run the software. The NM Application Suite includes both review and processing functionality and can be segmented into separate review and analysis configurations, such as a Planar and SPECT. The comprehensive tools and features provided with this product, will allow the technologist and/or physician to perform image review, processing of source data, post processing, hardcopy production, interpretation, report generation and contains the utilities necessary to support the workflow and data management between those activities. The system will support connectivity aspects necessary to import and export data as required to accomplish daily work scenarios.

This document is a 510(k) premarket notification for the "NM Application Suite" from Philips Medical Systems. It focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against specific acceptance criteria through a detailed study.

Therefore, much of the requested information regarding acceptance criteria, study design, sample sizes, ground truth, and expert involvement is not available in this document.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation: This document does not specify quantitative acceptance criteria or report device performance metrics (e.g., sensitivity, specificity, accuracy) from a clinical study. The 510(k) process for this type of device (image processing system) primarily focuses on demonstrating substantial equivalence to existing legally marketed devices based on similar intended use, technological characteristics, and system performance. Performance is implicitly assessed through comparison to predicate devices, not through a formal study with defined acceptance criteria.

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified. The document does not describe a specific clinical test set used for performance evaluation that would necessitate detailing data provenance. The assessment is based on a functional and technical comparison.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication method for the test set

• Adjudication Method: Not specified.

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

• MRMC Study Done? No. This document describes an image processing application suite, not an AI-assisted diagnostic tool that would typically undergo an MRMC study to assess reader improvement. The focus is on the software's functionality and its equivalence to other PACS/image processing systems.
• Effect Size: Not applicable.

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

• Standalone Performance Study Done? No, not in the sense of a clinical performance study with metrics like sensitivity/specificity. The document explains the device as a "suite of image display and processing applications," implying it's a tool for human professionals, not a fully autonomous diagnostic algorithm. Its "performance" is assessed through its functional equivalence (display, review, processing) to predicate devices.

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

• Type of Ground Truth: Not applicable. No specific ground truth establishment is described, as the evaluation is not based on a clinical performance study against a definitive diagnosis.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. The document describes an application suite for image display and processing, not a machine learning model that would typically have a "training set."

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

• Ground Truth Establishment for Training Set: Not applicable.

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