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McKesson Radiology Mammography Plus™ ("Mammography Plus"), is an accessory to McKesson Radiology Station™, a component of McKesson Radiology™. McKesson Radiology is medical image and information management software that is intended to receive, transmit, store, archive, 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-lossed 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 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.

Mammography Plus, an accessory to the McKesson Radiology Station, the radiology viewing component of McKesson Radiology, consists of image display and manipulation tools that help radiologists zoom in onto an image, interpret Computer Aided Detection (CAD) findings, navigate through and visualize digital mammography and Digital Breast Tomosynthesis (DBT) images, compare historically similar images, and configure their reading environment.

Mammography Plus uses the underlying capabilities of McKesson Radiology's diagnostic viewer, McKesson Radiology Station, to integrate specialized functionality into the diagnostic reading workflows. The McKesson Radiology Station viewer interfaces with the McKesson Radiology Platform to access patient and study information.

Here's an analysis of the provided text regarding the acceptance criteria and study for the McKesson Radiology Mammography Plus device:

The provided documents (K142850) do not contain typical acceptance criteria for specific performance metrics (e.g., sensitivity, specificity, AUC) or a dedicated clinical study demonstrating these metrics against ground truth. Instead, the submission focuses on verifying the device's functionality and image quality against established standards and its substantial equivalence to a predicate device for its intended use as a PACS accessory with mammography viewing and manipulation tools.

The "Performance Data" section describes verification and validation testing, which are typically aimed at ensuring the device meets its design specifications and complies with relevant regulatory and consensus standards.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not provide specific quantitative "acceptance criteria" related to diagnostic performance (e.g., sensitivity, specificity) for the device itself. The acceptance is based on functional verification, compliance with standards, and demonstration of substantial equivalence.

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

The document does not describe a "test set" in the context of a diagnostic performance study with patient data. The testing described is primarily focused on functional verification, image quality assessment, and usability. Therefore, there is no information provided on the sample size or data provenance (country, retrospective/prospective) for a clinical test set as one would typically find for a diagnostic AI algorithm.

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

As there is no mention of a diagnostic performance study with a clinical test set, there is no information provided on the number of experts or their qualifications for establishing ground truth for such a test set.

4. Adjudication Method for the Test Set

Since a diagnostic performance study with a clinical test set is not described, there is no information on adjudication methods.

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

The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance. The device is characterized as an accessory for image display and manipulation, not as a diagnostic AI tool that directly provides CAD findings (though it supports interpretation of CAD findings from other systems).

6. Standalone Performance Study

A standalone performance study (i.e., algorithm only without human-in-the-loop performance evaluation) for diagnostic accuracy is not described in the provided text. The device's performance is gauged by its ability to meet design specifications, adhere to industry standards for image quality and display, and its usability as an accessory.

7. Type of Ground Truth Used

For the described verification and validation, the "ground truth" is implicitly related to:

• Design Specifications: The device's intended functionality.
• Consensus Standards: (e.g., NEMA, AAPM, DICOM) which define expected behavior for image display and processing.
• Predicate Device Performance: Used for comparison in usability and image quality assessments.

There is no mention of pathology, outcomes data, or expert consensus on patient cases being used as ground truth for diagnostic accuracy in a clinical study.

8. Sample Size for the Training Set

The document does not describe a machine learning algorithm or AI model requiring a "training set." McKesson Radiology Mammography Plus is presented as software for image display, manipulation, and workflow integration for mammography images, including support for CAD findings (presumably from other CAD systems). Therefore, no information on a training set size is provided.

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

Since no training set is mentioned as part of the device's development, this information is not applicable and not provided.

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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:

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.

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