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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients.

The Philips MP75 IntelliVue patient monitor is intended for monitoring and recording of and to generate alarms for, multiple physiological parameters of adults, pediatrics and neonates in hospital environments. The monitor is not intended for home use. It is intended for use by health care professionals.

Here's an analysis of the provided text regarding the Philips MP75 IntelliVue patient monitor's acceptance criteria and study information:

Based on the provided 510(k) summary, the device is a patient monitor, and the submission is for a modification to an existing cleared device, not a new device. Therefore, the description focuses on demonstrating substantial equivalence to a predicate device rather than presenting a de novo clinical study with specific performance metrics against predefined acceptance criteria for a novel algorithm.

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not provide specific quantitative acceptance criteria or reported device performance metrics in the format of a table as would be expected for an AI/algorithm-based diagnostic device where performance metrics like sensitivity, specificity, or AUC are typically defined.

Instead, the submission states:

• "Pass/Fail criteria were based on the specifications cleared for the predicate devices"
• "test results showed substantial equivalence."
• "The results demonstrate that the Philips MP75 IntelliVue patient monitor meets all reliability requirements and performance claims."

This indicates that the "acceptance criteria" were broadly defined as meeting the established specifications and performance of the predicate devices. The "reported device performance" is essentially that it met these criteria, thus achieving substantial equivalence. No specific numerical performance values (e.g., accuracy, sensitivity for arrhythmia detection) are provided in this summary.

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

The document does not specify a sample size for a test set or data provenance (country of origin, retrospective/prospective). The testing involved "system level and regression tests as well as testing from the hazard analysis." This suggests engineering and functional testing rather than a clinical study with a patient data test set.

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

This information is not provided in the document. As this appears to be more of an engineering/functional validation rather than a clinical performance study, the concept of "ground truth" as established by medical experts for a diagnostic algorithm is not explicitly addressed.

4. Adjudication method 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

There is no indication of an MRMC comparative effectiveness study being performed. The device is a patient monitor, not an AI-assisted diagnostic tool for human readers.

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

This submission pertains to a patient monitor (a hardware device with integrated software), not a standalone algorithm. The "test results showed substantial equivalence" for the modified device (the patient monitor with the new application module) compared to the predicate device. Therefore, the testing described would inherently be of the integrated system.

7. The type of ground truth used

The document does not explicitly state the type of ground truth used. Given the nature of the device (a multi-parameter patient monitor) and the testing described (verification, validation, system-level, regression, hazard analysis), the "ground truth" would likely be derived from:

• Established specifications and technical standards: For signal acquisition, processing, and display accuracy.
• Simulated physiological signals: For testing various parameter measurements (e.g., ECG, blood pressure, SpO2).
• Benchmarking against predicate devices: Directly comparing outputs or performance to already cleared devices.

8. The sample size for the training set

This information is not applicable/not provided. The device is a patient monitor, and the submission describes a modification to an existing device. It does not refer to a new AI model that would require a "training set" in the context of machine learning. The "specifications cleared for the predicate devices" served as the baseline for the modification.

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

This information is not applicable/not provided, as there is no mention of a training set for an AI model.

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