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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. Intended for monitoring, recording and alarming of multiple physiological parameters of adults, pediatrics and neonates in hospital environments.

EASI 12-lead ECG is only for use on adult and pediatric patients.

ST Segment monitoring is restricted to adult patients only.

The transcutaneous gas measurement (tcpO2 / tcpCO2) is restricted to neonatal patients only.

The Philips MP40, MP50, MP60, MP70, and MP90 IntelliVue Patient Monitor.

The provided text describes a 510(k) summary for the Philips IntelliVue Patient Monitors, specifically focusing on the introduction of Release B.0 software and new models. However, it does not include the details typically found in a study proving a device meets specific acceptance criteria in the context of AI/ML or diagnostic performance. This document is a regulatory submission for a patient monitor, which is a hardware device with software, not a diagnostic AI algorithm.

Therefore, many of the requested categories related to AI/ML or diagnostic performance studies (like sample size for test/training sets, experts for ground truth, MRMC studies, standalone performance, etc.) are not applicable or not provided in this document.

Here's an attempt to answer the questions based only on the provided text, indicating where information is not available:

Acceptance Criteria and Device Performance Study for Philips IntelliVue Patient Monitors (K032858)

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

The document refers to acceptance criteria generally but does not provide a specific table of quantitative acceptance criteria or detailed performance metrics.

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: Not specified. The document mentions "system level tests, performance tests, and safety testing" but does not detail the number of cases, patients, or data points used in these tests.
• Data Provenance: Not specified.

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/Not specified. This document is for a patient monitor (hardware and general software), not a diagnostic AI algorithm requiring expert-established ground truth for a test set in the typical sense of a diagnostic performance study. The "ground truth" for a patient monitor would be its accurate measurement and display of physiological parameters, which is validated through engineering tests against known standards.

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

Not applicable/Not specified. Adjudication methods like 2+1 or 3+1 are typically used in studies involving human interpretation (e.g., image reading) where multiple experts resolve disagreements to establish a ground truth. This is not the type of testing described for a patient monitor.

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, and the testing described is not an MRMC comparative effectiveness study comparing human readers with and without AI assistance for interpretation.

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

Not applicable. While the device contains algorithms for monitoring various physiological parameters (e.g., arrhythmia detection, ST segment monitoring), the document does not describe standalone algorithm performance testing in the context of an AI/ML diagnostic or predictive algorithm being evaluated against a ground truth as typically understood for this type of question. The "performance" mentioned refers to the overall device's ability to accurately measure and display parameters.

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

Not specified in detail. For a patient monitor, the "ground truth" for performance testing would typically involve established reference standards, calibrated equipment, and simulated physiological signals to ensure accuracy of measurements (e.g., ECG, blood pressure, temperature, O2 saturation). The document states "Pass/Fail criteria were based on the specifications cleared for the predicate device," implying performance was compared against predetermined technical specifications.

8. The sample size for the training set

Not applicable/Not specified. The document describes a software release (Release B.0) for established patient monitors, not the development of a novel AI/ML algorithm that requires a "training set" in the context of machine learning.

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

Not applicable/Not specified, as there is no mention of a "training set" in the context of machine learning for an AI algorithm.

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