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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 healthcare facilities. The MP40 and MP50 are additionally intended for use in transport situations within healthcare facilities.

ST Segment monitoring is restricted to adult patients only.

The transcutaneous gas measurement (tcp02) is restricted to neonatal patients only.

The Philips MP40, MP50, MP60, MP70, MP80 and MP90 IntelliVue Patient Monitors. The modification is the introduction of Release D.00 software for the IntelliVue patient monitor devices, MP40, MP50, MP60, MP70, MP80, and MP90.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Philips IntelliVue Patient Monitors, Release D.00:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not contain a specific table of numerical acceptance criteria or detailed performance metrics. Instead, it offers a general statement about meeting "specifications cleared for the predicate device." Therefore, the table below reflects this general statement.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify the sample size used for the test set. It also does not specify the data provenance (e.g., country of origin, retrospective or prospective nature) for any of the testing. The testing described appears to be internal validation by the manufacturer.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The document does not provide any information regarding the number of experts used, nor their qualifications, for establishing ground truth. Given the nature of a patient monitor (measuring physiological parameters rather than diagnostic interpretation of images), the "ground truth" would likely be derived from established reference measurement devices rather than expert consensus on subjective data.

4. Adjudication Method for the Test Set

The document does not mention any adjudication method for the test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was done. The device is a patient monitor, not an AI-assisted diagnostic tool, so such a study would not be applicable in this context. There is no mention of AI assistance in the context of human readers.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device itself is a standalone patient monitor, designed to measure and display physiological parameters directly. The testing described (system level tests, performance tests, safety testing) directly relates to the standalone performance of the monitor. The document describes a standalone performance evaluation of the device's functionality.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used. However, for a patient monitor measuring physiological parameters, the ground truth would most typically be established through:

• Reference Measurement Devices: Using highly accurate and calibrated reference instruments (e.g., dedicated ECG machines for cardiac signals, precise pressure transducers for blood pressure, gas analyzers for gas measurements) to establish the true physiological values against which the monitor's readings are compared.
• Physical Simulators/Phantoms: Using controlled simulators that generate known physiological signals or conditions to test the monitor's accuracy and response.

It is highly unlikely that "expert consensus," "pathology," or "outcomes data" would be the primary ground truth for validating the fundamental performance of a patient monitor in the way it is for diagnostic imaging or clinical decision support AI.

8. The Sample Size for the Training Set

The document does not provide any information about a "training set." The device is a patient monitor with software (Release D.00) that likely incorporates established signal processing algorithms and control logic, rather than a machine learning model that requires a distinct training set in the modern sense. The text refers to "specifications cleared for the predicate device" as the basis for evaluation, implying a comparison to known benchmarks rather than an iterative learning process from a large data set.

9. How the Ground Truth for the Training Set Was Established

Since no training set is mentioned or implied for a machine learning context, this question is not applicable based on the provided document. The "ground truth" for the device's development and validation would be the physical and physiological accuracy standards it must meet, as defined by its intended use and regulatory requirements.

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