(84 days)
McKesson Radiology™ is medical image and information management software that is intended to receive, transmit, store, archive, retrieve, manage, display, print and process digital medical images, digital medical video and associated patient and medical information. McKesson Radiology includes a suite of standalone, web-enabled software components, and is intended for installation and use with off-the-shelf hardware that meets or exceeds minimum specifications.
McKesson Radiology Station™ is the primary software component used for processing and presentation of medical images on display devices with network access to McKesson Radiology. McKesson Radiology Station is intended to process and display lossless and non-lossless compressed medical images provided from DICOM conformant modalities such as X-Ray Radiography (including digital mammography), X-Ray Computed Tomography, Magnetic Resonance Imaging, Ultrasound, and Nuclear Medicine, as well as medical images obtained from other DICOM-compliant modalities.
Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations.
Mammographic images may only be interpreted using cleared monitors intended for mammography display.
McKesson Radiology is indicated for use by qualified healthcare professionals including, but not restricted to, radiologists, non-radiology specialists, physicians and technologists.
McKesson Radiology, a Picture Archiving and Communication System (PACS), consists of configurable software-only applications that receive, transmit. store, archive, retrieve, manage, display, print and process digital medical images, digital medical video and associated patient and medical information to aid in the day-to-day operations and workflow of clinicians and healthcare practitioners. It includes the primary diagnostic viewer McKesson Radiology Station, clinical-reference viewers, workflow tools and administrative tools. Although the primary McKesson Radiology functionalities, such as DICOM study acquisition, storage, and archival, are intended to be installed and used within a hospital or health care facility environment, the viewing, reporting and administrative functions are intended for use both inside and outside of these settings by qualified healthcare practitioners.
The provided text describes a 510(k) premarket notification for McKesson Radiology, a Picture Archiving and Communication System (PACS). The document focuses on demonstrating substantial equivalence to predicate devices rather than presenting a detailed study with specific acceptance criteria and performance data for a new, novel device feature.
Therefore, many of the requested elements for a study proving device acceptance criteria are not applicable or not present in the provided text. The McKesson Radiology device is primarily a software system for managing and displaying medical images, not a diagnostic algorithm with performance metrics like sensitivity and specificity against a ground truth.
Here's an analysis based on the information available:
1. Table of Acceptance Criteria and Reported Device Performance:
No specific quantitative acceptance criteria (e.g., minimum diagnostic accuracy, latency) and corresponding reported performance metrics are detailed in the document. The performance data section broadly states:
| Acceptance Criteria (Implicit) | Reported Device Performance |
|---|---|
| Meet all specifications | "McKesson Radiology functioned as intended" |
| Perform as intended | "McKesson Radiology functioned as intended" |
| Compliance with ISO 13485:2003, IEC 62304:2006, ISO 14971:2007/EN 14971:2012 | "Performance testing was conducted to verify compliance..." |
| Substantial Equivalence | "observed results demonstrate substantial equivalence with the predicate devices." |
2. Sample size used for the test set and the data provenance:
- Not explicitly stated. The document mentions "Verification and validation testing" and "Performance testing" but does not provide details on the specific data sets used for these tests, their sample sizes, or their provenance (e.g., country of origin, retrospective/prospective).
- Likely internal testing data: Given the nature of a PACS system, the testing would focus on functional correctness, system integration, DICOM compliance, and user interface validity, rather than clinical diagnostic accuracy per se. This would typically involve internal test cases and possibly simulated clinical scenarios.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable/Not stated. As this is a PACS system for managing and displaying images, the concept of "ground truth" as it relates to expert diagnosis of medical conditions in a test set is not directly relevant for demonstrating its primary functionality.
- The validation would likely involve experts in software testing, medical imaging informatics, and potentially medical professionals (radiologists, technologists) to verify usability and correct display of images, but not to establish a diagnostic ground truth for algorithmic performance.
4. Adjudication method for the test set:
- Not applicable/Not stated. No information is provided regarding an adjudication method, as the testing described is not a clinical study involving diagnostic interpretations.
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:
- No. An MRMC study is not mentioned. This type of study is typically conducted for AI-powered diagnostic aids, which McKesson Radiology is not described as. The document focuses on the system's ability to "receive, transmit, store, archive, retrieve, manage, display, print and process digital medical images."
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Not applicable. TheMcKesson Radiology is a PACS, an infrastructure for image management and display, not a standalone diagnostic algorithm. Its function is to facilitate human interpretation, not to provide automated diagnoses without human interaction.
7. The type of ground truth used:
- Not applicable/Implicitly functional correctness. The "ground truth" for this device would be its adherence to technical specifications, DICOM standards, and the correct rendering and management of medical images. It's not a clinical ground truth for diagnostic accuracy.
8. The sample size for the training set:
- Not applicable. McKesson Radiology is described as software for image and information management, not a machine learning model that requires a training set.
9. How the ground truth for the training set was established:
- Not applicable. As no training set is relevant for this type of device, no ground truth for a training set was established.
§ 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).