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Horizon Cardiology™ is an integrated multimodality image and information system designed to perform the necessary functions required for import, export, storage, archival, review, analysis, quantification, reporting and database management of digital cardiovascular images.

Horizon Cardiology™ is intended for use in the Cardiology, Radiology or other departments throughout the healthcare facility and may be part of a larger PACS configuration.

Horizon Cardiology™ offers support for third party plug-ins in order to enable the use of commercially available tools for analysis, quantification and reporting.

Horizon Cardiology™ is an image processing system. The device consists of the following components and accessories: software application; database server; web server; application server; image and document storage server and media; long term archive and disaster recovery media; and client application workstation.

The provided 510(k) summary for McKesson Israel Ltd.'s Horizon Cardiology™ does not contain specific acceptance criteria or a study that evaluates the device against such criteria in terms of clinical performance or accuracy beyond confirming its intended functionality.

Instead, the submission focuses on substantial equivalence to predicate devices based on intended use, technological characteristics, and principles of operation. The "Performance Data" section states that "Verification and validation testing was performed on Horizon Cardiology™ to ensure it met all specifications. The device was further validated to ensure that it performs as intended. In all instances, Horizon Cardiology™ functioned as intended and the results observed demonstrate substantial equivalence with the predicate devices." This suggests internal testing for functionality and specifications, but no detailed clinical performance study with defined acceptance criteria is presented.

Therefore, the requested information cannot be fully extracted from the provided text. However, based on the information given, here's what can be inferred and what is explicitly missing:

1. Table of Acceptance Criteria and Reported Device Performance

Not applicable in the context of the provided document. The document does not specify quantitative acceptance criteria for clinical performance (e.g., sensitivity, specificity, accuracy for a specific diagnostic task). The "performance" discussed broadly refers to the device functioning as intended in comparison to predicate devices, not against specific, measurable clinical outcomes.

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

Not specified. The document mentions "Verification and validation testing" but does not detail the nature of this testing, the size of any test sets (clinical or technical), or the provenance (country of origin, retrospective/prospective) of any data used.

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

Not applicable/Not specified. Since no clinical performance study with a test set and ground truth establishment is described, this information is not present.

4. Adjudication Method for the Test Set

Not applicable/Not specified. As no clinical performance study with a test set is described, there is no mention of an adjudication method.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. The document does not describe a MRMC comparative effectiveness study where human readers' performance with and without AI assistance is evaluated.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance Study

No. The device is described as an image and information system for import, export, storage, archival, review, analysis, quantification, reporting, and database management. It's a system to support cardiovascular imaging workflows, not a standalone AI algorithm designed for automated diagnostic tasks. Therefore, a standalone performance study in the sense of an "algorithm only" evaluation is not relevant or described.

7. Type of Ground Truth Used

Not applicable/Not specified. Without a clinical performance study, there is no mention of the type of ground truth used (e.g., expert consensus, pathology, outcomes data). The "ground truth" for the device's functionality would likely be adherence to technical specifications and successful execution of its stated features.

8. Sample Size for the Training Set

Not applicable/Not specified. As this is not presented as an AI/machine learning diagnostic device in the sense of a predictive algorithm, there is no mention of a training set or its size.

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

Not applicable/Not specified. Similar to the above, without a training set for an AI algorithm, there is no information on how its ground truth would be established.

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