(30 days)
McKesson 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 and information from other data sources.
McKesson Cardiology™ is intended for use in the Cardiology, Radiology or other departments throughout the healthcare facility and distributed locations and may be part of a larger PACS configuration.
McKesson Cardiology™ offers support for third party plug-ins in order to enable the use of commercially available tools for analysis, quantification and reporting.
McKesson Cardiology™ is intended to assist trained professionals in the viewing and diagnostic interpretation of images and other information for the diagnosis and treatment of cardiac and vascular disease.
McKesson 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.
New additional features and functionalities in this version and subject of this submission include:
- MC-D (distributed web client application) patient list was redesigned as a browser based procedure list application
- Procedure priority indication new display of procedure priority level
- MRN aggregation option to review aggregated list of all patient procedures in MC-D for patients with multiple procedures from different facilities
- VNA Interoperability Imaging Object Change Management (IOCM)
- Vascular report redesigned as a browser based module
- Multi-facility Support
- New platform qualifications
- EMR integration enhancements
- -Added security, privacy and cybersecurity enhancements
The provided text describes a 510(k) submission for "McKesson Cardiology™" seeking substantial equivalence to a previously cleared version of the same device. This type of submission, particularly for a Picture Archiving and Communications System (PACS), typically focuses on software functionality, workflow improvements, and ensuring that new features do not adversely affect existing safety or effectiveness. As such, it does not contain the kind of detailed clinical study data generally associated with AI/ML-driven diagnostic devices that would involve acceptance criteria for diagnostic performance, ground truth establishment by multiple experts, or multi-reader multi-case studies.
Therefore, I cannot provide all the requested information. However, based on the provided text, I can infer and state the following:
1. A table of acceptance criteria and the reported device performance
The document states: "Verification and validation testing was performed on McKesson Cardiology to ensure it met all specifications." and "In all instances, McKesson Cardiology functioned as intended and the observed results demonstrate substantial equivalence with the predicate devices."
However, the specific "acceptance criteria" for performance metrics like sensitivity, specificity, or AUC, as one would see for an AI diagnostic algorithm, are NOT defined or presented in the document. The performance described is functional and technical, not diagnostic efficacy.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document states: "No clinical studies were necessary to support substantial equivalence." This means there was no clinical "test set" in the sense of a dataset of patient cases used for diagnostic performance evaluation. The testing performed was technical verification and validation, and usability testing.
- Sample Size for Test Set: Not applicable as no clinical study was conducted.
- Data Provenance: Not applicable.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
- Number of Experts: Not applicable, as no clinical ground truth was established for diagnostic performance.
- Qualifications of Experts: Not applicable.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Adjudication Method: Not applicable.
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: No, an MRMC study was not conducted. The device is a PACS system designed to assist professionals in viewing and interpreting images, not an AI intended to improve human reader performance in a quantifiable way like a diagnostic algorithm.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Standalone Performance: Not applicable. The device is a PACS, an image processing and information system, not an AI algorithm that produces an independent diagnostic output. Its purpose is to present information to human users.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Type of Ground Truth: Not applicable, as no clinical ground truth was established for diagnostic performance. The "ground truth" for this device would be its adherence to technical specifications, proper functioning, and user experience, validated through engineering and usability testing.
8. The sample size for the training set
- Sample Size for Training Set: Not applicable. This document describes a PACS system, not an AI/ML device that requires a training set of data.
9. How the ground truth for the training set was established
- Ground Truth Establishment for Training Set: Not applicable.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).