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For central monitoring of multiple adult, pediatric and neonatal patients; and where the clinician decides to monitor cardiac arrhythmia of adult, pediatric, and neonatal patients and/or ST segment of adult patients to gain information for treatment, to monitor adequacy of treatment, or to exclude causes of symptoms.

The modification is a software-based change that provides database server access via the hospital intra/internet system.

This document is a 510(k) Pre-Market Notification for a software device, the Viridia Information Software for Model M3154A Database Server, Release B.02. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than comprehensive clinical studies with detailed acceptance criteria and performance metrics typically found in AI/ML device submissions.

Based on the provided text, here's what can be extracted and what cannot:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative acceptance criteria or detailed performance metrics in the way modern AI/ML devices do (e.g., sensitivity, specificity, AUC). Instead, it relies on demonstrating substantial equivalence to a predicate device.

Acceptance Criteria (Implied from the document):

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not specified. The document mentions "system level tests, integration tests, environmental tests, and safety testing from hazard analysis," but does not provide specific data sample sizes or details about patient data used in testing.
• Data Provenance: Not specified. As this is a software update primarily focused on database access via intra/internet, details about patient data origin are not a focus of this submission.
• Retrospective or Prospective: Not specified. The device is used for "read-only viewing of patient physiologic data, including retrospective review applications." The testing methodology does not detail whether new prospective data was used for validation.

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)

• Not applicable. This device is a database server software for viewing patient physiological data, not an AI/ML diagnostic or prognostic tool that requires expert-established ground truth on medical images or clinical outcomes.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not applicable. As above, this is not a diagnostic device requiring adjudicated ground truth for performance evaluation. The testing involved verifying software functionality and equivalence to a predicate.

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. This is not an AI-assisted diagnostic device. The study described focuses on software functionality, reliability, and substantial equivalence to a predicate device for displaying patient data.

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

• Yes, in a sense. The "device" itself is the software, which performs its functions (database access, data display) without human intervention in its core operation. However, its purpose is to present data for human interpretation, so it's inherently part of a human-in-the-loop clinical workflow for data review. The described testing verifies the software's stand-alone functionality.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Not applicable in the typical sense for medical diagnostic devices. The "ground truth" for this device's performance would be the successful and accurate display of patient physiological data as recorded by monitoring devices, consistent with the predicate device's functionality. The testing confirmed that the software correctly accessed, displayed, and allowed review of this data according to its specifications.

8. The sample size for the training set

• Not applicable. This device is not an AI/ML model that undergoes a "training" phase with data. It's conventional software for data management and display.

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

• Not applicable. As above, there is no "training set" for this type of software.

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