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The ResolutionMD™ Cardiac Product Family is a software based Picture Archiving and Communication System (PACS) used with general purpose computing hardware for the display and 3D visualization of medical image data. It provides for communication, storage, processing, rendering, and display of DICOM 3.0 compliant image data derived from various sources including CT and MRI.

The ResolutionMD™ Cardiac Product Family is intended for use as a diagnostic, review. and analysis tool by trained professionals such as physicians, technologists, and nurses. When interpreted by a trained physician, reviewed images may be used as an element for diagnosis. It is the user's responsibility to ensure that the software is installed on appropriate hardware and that image quality is suitable for the clinical application.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using an FDA approved monitor that offers at least 5 Mpixel resolution and meets other technical specifications reviewed and accepted by FDA.

The ResolutionMD™ Cardiac Product Family is a software based Picture Archiving and Communication System (PACS) used with general purpose computing hardware for the display and 3D visualization of medical image data. It provides for communication, storage, processing, rendering, and display of DICOM 3.0 compliant image data derived from various sources including CT and MRI. The ResolutionMD™ software takes advantage of dedicated graphics hardware to speed the creation 3D rendered images.

The ResolutionMDTM Cardiac Product Family is available in a Microsoft Windows version and in a Macintosh OS version. Both versions offer similar functionality. Available functions include DICOM communication, display of 2D images in original planes, computation and display of rendered 3D images and maximal intensity projection (MIP) images, 2D and 3D image measurements, calcium scoring, and coronary artery analysis. The user controls these functions with a system of interactive menus and tools.

A hazard analysis has been conducted and the level of concern has been classified as minor. The ResolutionMD™ Cardiac Product Family software will be extensively tested on supported platforms by members of the development and quality control team prior to beta testing. Beta testing by trained cardiology professionals and potential customers will be completed prior to product release. The release version of the software will be required to pass all tests considered critical in terms of patient safety and demonstrate an overall acceptable performance for release as determined by the predefined release criteria.

The provided 510(k) summary for the ResolutionMD™ Cardiac Product Family does not contain specific acceptance criteria or a dedicated study demonstrating the device's performance against such criteria. Instead, it focuses on establishing substantial equivalence to a predicate device (Vitrea2) based on functional and technical similarities.

However, based on the information provided, we can infer some aspects and highlight what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state acceptance criteria in terms of quantitative performance metrics (e.g., accuracy, sensitivity, specificity, processing time, etc.). The "performance" described is largely a comparison of features and capabilities with the predicate device.

Missing Information: There are no quantitative performance metrics such as accuracy for calcium scoring, precision of measurements, speed of rendering, or comparison of diagnostic outcomes using the device versus the predicate or traditional methods.

2. Sample Size Used for the Test Set and Data Provenance:

The document mentions "beta testing by trained cardiology professionals and potential customers" as part of the validation process. However, it does not provide any details regarding:

• The sample size of medical images or patient cases used in this testing.
• The data provenance (e.g., country of origin of the data, retrospective or prospective nature of the beta testing data).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:

The document states that "beta testing by trained cardiology professionals" will be completed. However, it does not specify:

• The number of experts involved in establishing ground truth for the test set.
• The qualifications of these experts (e.g., "radiologist with 10 years of experience" is not mentioned, only "trained cardiology professionals").

4. Adjudication Method:

The document does not mention any specific adjudication method (e.g., 2+1, 3+1, none) used for the test set during beta testing or any other validation.

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. Therefore, there is no information on the effect size of how much human readers improve with AI vs. without AI assistance, as this is a PACS software and not strictly an AI-driven diagnostic aid in the modern sense.

6. Standalone Performance Study:

Since this is a PACS system designed for human-in-the-loop use by trained professionals, a standalone (algorithm only) performance study in the sense of an AI algorithm making a diagnosis without human intervention is not applicable or mentioned. The "device performance" relies on the trained professional's interpretation using the software.

7. Type of Ground Truth Used:

While "beta testing by trained cardiology professionals" implies a form of expert review, the exact "type of ground truth used" (e.g., expert consensus, pathology, outcomes data) for validating the software's features like calcium scoring or artery detection is not explicitly stated. It's likely that established clinical reporting for calcium scores and visual assessment for artery detection would serve as the implicit ground truth.

8. Sample Size for the Training Set:

The document does not mention a training set sample size. This is expected as the device, introduced in 2007, is a PACS software for visualization and analysis, not an AI/machine learning model in the contemporary sense that typically requires a large training set. Its development would have focused on engineering robust algorithms for image processing and rendering, rather than learning from data.

9. How the Ground Truth for the Training Set Was Established:

As there is no mention of a "training set" in the context of machine learning, the question of how ground truth was established for a training set is not applicable. The underlying algorithms for features like calcium scoring would be based on established clinical methodologies (e.g., Agatston method) rather than being "trained" on data with ground truth.

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