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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 and recording of and to generate alarms for multiple physiological parameters of adults, pediatrics and neonates in hospital environments. The MP2, X2, MP5, MP20, MP30, MP40, and MP50 are additionally intended for use in transport situations within hospital environments. The MP2, X2 and MP5 are also intended for use during patient transport outside of a hospital environment.

The Philips MP2, X2, MP5, MP20, MP30, MP40, MP50, MP60, MP70, MP80, and MP90 IntelliVue Patient Monitors and IntelliVue XDS.

The provided text describes a 510(k) premarket notification for Philips IntelliVue Patient Monitors with software release G.02, which is deemed substantially equivalent to previously cleared predicate devices. The document does not contain specific acceptance criteria, reported device performance metrics in a table, or detailed information about a study proving the device meets acceptance criteria in the way typically found in an AI/ML device submission (e.g., performance against a ground truth dataset).

This submission appears to be for a software update to an existing line of patient monitors, not a novel AI/ML diagnostic or prognostic device that would require performance metrics like sensitivity, specificity, or AUC against a ground truth. The testing described focuses on establishing equivalence to predicate devices and ensuring the software update maintains performance and reliability based on existing specifications.

Therefore, many of the requested fields cannot be directly extracted from the provided text because they are not relevant to this type of device submission or are not detailed in the summary.

Here's an attempt to answer the questions based only on the provided text, highlighting where information is absent:

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

The provided text does not contain a table of specific acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) or reported device performance metrics for the software update. Instead, it states: "Pass/Fail criteria were based on the specifications cleared for the predicate devices and test results showed substantial equivalence." and "The results demonstrate that the Philips IntelliVue Patient Monitors meet all reliability requirements and performance claims." These are general statements rather than specific quantifiable 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)

The text does not specify a sample size for any test set or provide details about data provenance (e.g., country of origin, retrospective/prospective). It mentions "system level and regression tests as well as testing from the hazard analysis" but not the dataset characteristics.

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)

This information is not provided. Given this is a software update for physiological monitors, the "ground truth" would likely be established through physical testing and comparison with established measurement techniques, rather than expert consensus on interpretive data.

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

This information is not provided.

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 comparative effectiveness study is mentioned. This type of study is typically relevant for interpretative AI devices that assist human readers (e.g., radiologists, pathologists). The devices described are physiological monitors.

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

The submission describes a software update for patient monitors, which inherently operate in a "standalone" fashion by measuring and displaying physiological parameters and generating alarms. The testing focused on performance, functionality, and reliability against predicate device specifications, which implies evaluating the device's inherent operation rather than its impact on human performance in an interpretive task. No specific "standalone performance study" with detailed metrics like those for an AI algorithm is provided in the summary.

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

The text doesn't specify the type of "ground truth." For physiological monitors, ground truth would typically refer to the true physiological values measured by highly accurate reference standards or established clinical methods, against which the device's measurements (e.g., heart rate, blood pressure, SpO2) are compared. The phrase "Pass/Fail criteria were based on the specifications cleared for the predicate devices" indicates that the new software was evaluated against the performance standards already established for the previous versions of the monitors.

8. The sample size for the training set

This information is not provided. The document describes a software update for patient monitors and does not mention any machine learning or AI components that would require a "training set" in the typical sense.

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

This information is not provided, as no training set is mentioned in the context of this software update.

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