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The Centricity PACS Plus system is used to transmit, store and display images throughout a clinical environment. The Centricity PACS Plus is an image display software application that is intended for use by qualified physicians and other personnel for reading, diagnostic review, and analysis of digital images acquired from imaging devices such as CT, MR, CR, DX, MG, US, NM, PET, and other devices. As a part of the PACS Plus system the Centricity RA1000 is used to view digital images that are obtained from a GE Centricity PACS System, or another DICOM device.

See Attachment #2

The provided text is a 510(k) Summary for the Centricity PACS Plus device. This document focuses on demonstrating substantial equivalence to predicate devices for regulatory clearance, rather than presenting a detailed study with acceptance criteria and performance metrics typically found in clinical trials or validation studies for novel AI/CADe devices.

Therefore, much of the requested information regarding acceptance criteria, study details, sample sizes, ground truth establishment, expert qualifications, and MRMC effectiveness studies is not available in the provided text.

Based on the available information, here's what can be extracted:

1. Table of acceptance criteria and the reported device performance:

The document does not explicitly state quantitative acceptance criteria or detailed performance metrics. The core "acceptance criterion" for this type of 510(k) is demonstrating substantial equivalence to existing legally marketed predicate devices. The performance is reported as:

2. Sample size used for the test set and the data provenance:

• Sample size for test set: Not specified.
• Data provenance: Not specified. This document refers to displaying images acquired from various imaging devices (CT, MR, CR, DX, MG, US, NM, PET), but does not detail the origin of data used for any testing.
• Retrospective or prospective: Not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not specified. The document does not describe the establishment of a ground truth for a test set in the context of device performance evaluation.

4. Adjudication method for the test set:

• Not specified.

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 MRMC study mentioned. This document pertains to a Picture Archiving and Communication System (PACS), which is software for transmitting, storing, and displaying images, not typically a device that directly assists with diagnostic interpretation in the same way a CADe system would, hence an MRMC study for reader improvement with AI assistance is not relevant or described here.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Not applicable/Not mentioned. This device is an image display software application intended for use by qualified personnel, implying human-in-the-loop use. It is not an algorithm that performs standalone diagnostic tasks without human oversight.

7. The type of ground truth used:

• Not specified. This document primarily asserts functionality and comparability for image display and storage, rather than diagnostic accuracy that would require a ground truth for a specific disease or condition.

8. The sample size for the training set:

• Not applicable/Not specified. This document describes a PACS system, which is a software application for image management, not a machine learning or AI algorithm that typically has a "training set" in the conventional sense.

9. How the ground truth for the training set was established:

• Not applicable/Not specified. As there's no mention of a training set, the establishment of its ground truth is not discussed.

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