(35 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.
The version of McKesson Cardiology, subject of this 510(k), allows for the provision of additional information from expanded data sources to assist trained healthcare professionals throughout the healthcare facility and distributed locations in diagnosing and treating cardiac and vascular diseases.
McKesson Cardiology™ is an image processing system. The device consists of the following components and accessories: software application; database server; application server; image and document storage server and media; long-term archive and disaster recovery media; and client application workstation.
The version of McKesson Cardiology, subject of this 510(k) includes enhancements and new features including those for supporting dictation and streamlining user workflow for documenting, charting and trending procedural related data for Cath and other reporting; Statistical data collection of non-invasive features use for adoption considerations; Security; DICOM display view; Storage and archival enhancements; Support for stress modalities XML data import; EP modalities data import and reporting; and, importing lab results and exporting procedure medications using standard HL7.
Here's an analysis of the provided text regarding the acceptance criteria and study information for McKesson Cardiology™.
It's important to note that the provided document is a 510(k) summary for a Picture Archiving and Communication System (PACS) related to cardiology, and therefore, it focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with detailed acceptance criteria for a diagnostic algorithm.
Based on the document, here's what can be extracted:
1. Table of Acceptance Criteria and Reported Device Performance
Formal acceptance criteria with specific performance metrics (e.g., sensitivity, specificity, AUC) for a diagnostic output are not provided in this document. The "performance" described is centered around functional verification and validation of a medical imaging and information system.
| Acceptance Criteria (Inferred from described tests) | Reported Device Performance |
|---|---|
| Compliance with specified design requirements (ISO 13485:2003, IEC 62304:2006, ISO 14971:2007) | "McKesson Cardiology functioned as intended and the observed results demonstrate substantial equivalence with the predicate devices." |
| DICOM conformance (NEMA 3.1-3.20 (2011) standards) | "DICOM conformance testing was performed to verify compliance... In all instances, McKesson Cardiology functioned as intended..." |
| Meeting all specifications for software application, database, and storage | "Verification and validation testing was performed on McKesson Cardiology to ensure it met all specifications." |
| Usability for features (where applicable) | "Usability testing was performed, where applicable." |
| Functioning as intended | "The device was further validated to ensure that it performs as intended." |
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify a "test set" in the context of a diagnostic performance study. The testing described (verification, validation, usability, DICOM conformance) would typically involve internal data or simulated data to ensure system functionality, not a clinical dataset for evaluating diagnostic accuracy.
- Sample size for test set: Not applicable/not provided for diagnostic performance.
- Data provenance: Not applicable/not provided for diagnostic performance.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
This information is not provided as the submission is not for a diagnostic algorithm requiring ground truth established by experts.
4. Adjudication Method for the Test Set
This information is not provided as the submission is not for a diagnostic algorithm requiring adjudicated ground truth.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No, a MRMC comparative effectiveness study was not done. The document explicitly states: "No clinical studies were necessary to support substantial equivalence." This type of study demonstrates improved human reader performance with AI assistance, which is not relevant for this PACS device's substantial equivalence claim.
- Effect size: Not applicable.
6. Standalone (Algorithm Only) Performance Study
- No, a standalone performance study was not done. The device (McKesson Cardiology™) is described as an "integrated multimodality image and information system" intended to "assist trained professionals in the viewing and diagnostic interpretation of images and other information." It is not presented as a standalone diagnostic algorithm.
7. Type of Ground Truth Used
- Not applicable in the context of a diagnostic performance study. The ground truth for the verification and validation tests would be the expected functional behavior or standard compliance as defined by the design specifications and regulatory standards (e.g., DICOM standard conformance).
8. Sample Size for the Training Set
- Not applicable. This device is a PACS, not an AI/ML algorithm that requires a training set. The "development" of such a system involves software engineering and system integration, not machine learning model training.
9. How the Ground Truth for the Training Set Was Established
- Not applicable. As there is no training set mentioned for an AI/ML model, the ground truth establishment is irrelevant in this context.
§ 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).