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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 alarmine of multiple physiological parameters of adults, pediatrics and memates in healthcare facilities. The MP20, MP30, 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 / tcpCO2) is restricted to neonatal patients only.

The names of the devices are the Philips MP20, MP30, MP40, MP50, MP60, MP70, MP80 and MP90 IntelliVue Patient Monitors. The modification is the introduction of Release D.02 software into the IntelliVue Patient Monitor devices, MP20, MP30, MP40, MP50, MP60, MP70, MP80 and MP90.

This 510(k) summary (K060221) describes the Philips MP20, MP30, MP40, MP50, MP60, MP70, MP80, and MP90 IntelliVue Patient Monitors, Release D.02. This submission is for a software modification (Release D.02) to existing, legally marketed predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance:

The document implicitly states that the performance criteria for the modified devices were based on the predicate devices and other previously cleared submissions. The testing showed "substantial equivalence." However, explicit numerical acceptance criteria for specific physiological parameters are not provided in this summary. Instead, the focus is on demonstrating that the new software release maintains the safety and effectiveness of the previously cleared devices.

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

• Sample Size: The document does not specify a numerical sample size for the test set used in "system level tests, performance tests, Pass/Fail tests, and safety testing."
• Data Provenance: Not specified. However, given that Philips Medizin Systeme Boeblingen GmbH (Germany) is the submitter, it is likely that parts of the testing were conducted in Germany. The nature of the testing (performance tests, safety tests) suggests a combination of simulated and possibly real-world data, but this is not explicitly stated. The study appears to be retrospective in the sense that it evaluates the new software's performance against existing benchmarks and predicate device performance, rather than a new, prospective clinical trial.

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

Not applicable. This submission is for a software modification to an existing device, focusing on demonstrating substantial equivalence to its predicate. The testing described (system level, performance, pass/fail, safety) would typically involve engineering and quality assurance personnel, not clinical experts establishing ground truth in a diagnostic context.

4. Adjudication Method for the Test Set:

Not applicable. The testing described does not involve human interpretation requiring adjudication. It's a technical verification of device functionality and performance.

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

No, an MRMC comparative effectiveness study was not done. This type of study is typically used for diagnostic devices where human readers interpret medical images or data, and the AI assists in that interpretation. The IntelliVue Patient Monitors are monitoring devices, and the submission is for a software update to ensure existing functionality is maintained.

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

The testing described (system level tests, performance tests, Pass/Fail tests, and safety testing from hardware and software analysis) is inherently a standalone assessment of the device's technical performance with the new software. It evaluates the algorithm and device's capabilities independent of a human operator's direct interpretation of the raw output for diagnostic purposes. The device is a "patient physiological monitor (with arrhythmia detection or alarms)," indicating algorithmic processing of physiological signals.

7. The Type of Ground Truth Used:

The ground truth for the performance tests would be based on:

• Known input signals: For performance and accuracy tests, the device would likely be fed known, precisely controlled physiological signals to verify its measurement accuracy, alarm thresholds, and data processing.
• Predicate device specifications: The performance of the modified device would be compared against the established specifications and performance characteristics of the legally marketed predicate devices.
• Safety standards: Compliance with relevant safety and electrical standards forms a critical part of the ground truth.

8. The Sample Size for the Training Set:

Not applicable. This document describes a software update for a physiological monitor, not a machine learning or AI model that requires a "training set" in the conventional sense. The "training" of the device's algorithms would have occurred during the development of the original predicate devices, if applicable.

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

Not applicable, as there is no mention of a "training set" in the context of this software update. The algorithms for physiological monitoring (e.g., arrhythmia detection, ST segment analysis) are typically based on established medical science and engineering principles, not statistical learning from a large, annotated training dataset for this type of device at the time of this submission (2006).

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