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The Hewlett-Packard Viridia M3/M4 Patient Monitoring System, Rel.B is intended for monitoring, recording, and alarming of multiple physiological parameters' of adults, pediatrics, and neonates in the hospital and medical transport environments.

1. List of supported measurements ECG (a) Respiration (b) Invasive blood pressure (c) Non-invasive blood pressue ( વ ) SpO2 and Pleth (e) Temperature ( ) ) CO2 ( વે )

The modification is the addition of a firmware and software based change that involves the addition of the M3016A Module to the portable Viridia M3/M4 Patient Monitor System to allow CO-Pressure, and Temperature measurements with the unit.

The provided text describes a 510(k) summary for the Hewlett-Packard Viridia Patient Monitor M3000A/M3046A with M3016A, which is primarily a patient monitoring system. The information focuses on the substantial equivalence of the new device (with the M3016A module) to previously cleared HP devices.

However, the provided text does not contain the detailed information required to fill out all the sections of your request regarding acceptance criteria and a specific study proving the device meets those criteria, especially in the context of an AI/algorithm-driven device. The document is for a patient monitor and its new module, not an AI diagnostic tool as implied by many of your questions (e.g., ground truth, MRMC study, standalone algorithm performance).

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

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

The document broadly states: "Pass/Fail criteria were based on standards, where applicable, and on the specifications cleared for the predicate devices. The test results showed substantial equivalence." This is a very high-level statement and does not provide specific numerical acceptance criteria or performance metrics for parameters like sensitivity, specificity, accuracy, or other typical AI performance measures.

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

The text states: "Verification, validation, and testing activities were conducted to establish the performance and reliability characteristics of the new module using simulated systems."
This indicates that a real-world patient test set was not used. No sample size for a test set is mentioned, nor is data provenance or whether it was retrospective/prospective, as it was based on simulated systems.

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. Ground truth from experts would be relevant for diagnostic devices interpreting findings, not for a patient monitor measuring physiological parameters with simulated systems.

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

Not applicable. This is relevant for expert-adjudicated ground truth in diagnostic studies, which was not performed here.

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

Not applicable. This device is a patient monitor, not an AI-assisted diagnostic tool that would involve human readers interpreting images or data with and without AI assistance.

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

The device is a patient monitor. Its "performance" refers to its ability to accurately measure physiological parameters. The text suggests testing was done on the system and its components. While it functions without human "interpretation" of its measurements in the diagnostic sense, it's not an algorithm in the sense of a standalone diagnostic AI. Its tests were against established specifications.

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

For the verification and validation, the "ground truth" or reference was likely derived from the specifications and standards used for the simulated systems, ensuring the device accurately measures the intended physiological parameters within acceptable tolerances. This is based on technical specifications rather than clinical ground truth like pathology or expert consensus.

8. The sample size for the training set

Not applicable. This device is a patient monitor, not an AI model that requires a training set in the machine learning sense. The "simulated systems" were used for testing, not training.

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

Not applicable, as there was no training set in the AI/machine learning context.

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