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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 environments. The MP20, MP30, MP40 and MP50 are additionally intended for use in transport situations within healthcare environments. ST Segment monitoring is restricted to adult patients only. The transcutaneous gas measurement (tcp02 / tcpCQ2) is restricted to neonatal patients only.

The Philips MP20, MP30, MP40, MP50, MP60, MP70, and MP90 IntelliVue Patient Monitors with 802.11 Wireless LAN. The modification is a change that creates, an optional, 802.11 wireless network adapter for the Philips MPxx IntelliVue patient monitors.

The provided 510(k) summary for the Philips IntelliVue Patient Monitors with 802.11 Wireless LAN does not contain detailed information about specific acceptance criteria and a study proving the device meets those criteria, as typically found for AI/ML-based medical devices.

Instead, this submission is for a modification (the addition of an 802.11 wireless network adapter) to existing patient monitors. The focus is on demonstrating substantial equivalence to previously cleared predicate devices, rather than a de novo evaluation of novel performance.

Here's a breakdown of what can be extracted and what is not present in the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not explicitly detailed in the provided document in the context of specific quantitative metrics for the wireless LAN performance.

The document states: "Verification, validation, and testing activities establish the performance, functionality, and reliability characteristics of the modified device with respect to the predicate. Testing involved system level tests, performance tests, and safety testing from hazard analysis. Pass/Fail criteria were based on the specifications cleared for the predicate device and test results showed substantial equivalence."

This indicates that:

• Acceptance Criteria: Were based on "specifications cleared for the predicate device." These specific criteria (e.g., latency, throughput, signal strength, reliability) are not listed.
• Reported Device Performance: The summary states "test results showed substantial equivalence" and "The results demonstrate that the Philips IntelliVue Patient Monitor meets all reliability requirements and performance claims." No quantitative performance data for the wireless LAN is given.

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

This information is not provided for the specific wireless LAN testing. The document generally refers to "system level tests, performance tests, and safety testing" without detailing the sample sizes (e.g., number of test cases, duration of tests, number of devices tested) or data provenance.

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

This information is not applicable/not provided. The device is an integrated patient monitor with a wireless component, not an AI/ML diagnostic or prognostic tool that requires expert-established ground truth for its core function. The "ground truth" here would relate to engineering specifications and performance standards for wireless communication and patient monitoring, not clinical expert consensus on interpretations of data.

4. Adjudication Method for the Test Set

This information is not applicable/not provided. Similar to point 3, adjudication methods like 2+1 or 3+1 statistical consensus are typically used for clinical image or signal interpretation ground truth, not for evaluating the performance of a wireless network adapter in a medical device.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done

This information is not applicable/not provided. MRMC studies are used to assess the diagnostic performance of human readers, often with and without AI assistance, usually for interpreting medical images or complex data. This submission is for a hardware modification (wireless LAN) to a patient monitor, not a diagnostic AI system.

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

This information is not applicable/not provided. The device is a patient monitor, which operates with human interaction and is not solely an "algorithm" in the conventional AI sense. The wireless LAN is a component of a larger system.

7. The Type of Ground Truth Used

The "ground truth" for the testing described would be based on engineering specifications and established performance standards for patient monitoring devices and wireless communication. For example, standards for signal integrity, data transmission rates, latency, security, and reliability in a medical environment would serve as the ground truth against which the wireless LAN's performance was measured. No clinical outcomes data or pathology reports would typically be relevant for this type of device modification.

8. The Sample Size for the Training Set

This information is not applicable/not provided. The document describes a hardware modification and associated performance testing, not the development or training of an AI/ML algorithm. Therefore, there is no "training set" in this context.

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

This information is not applicable/not provided for the same reasons as point 8.

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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters patients. Intended for monitoring, recording and alarming of of multiple physiological parameters of adults, pediatrics and neonates in hospital environments. The MP20, MP30, MP40 and MP50 are additionally intended for use in transport situations within hospital environments.

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

ST Seqment 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, and MP90 IntelliVue Patient Monitor. The modification is the introduction of Release B.1.1 software for the IntelliVue patient monitor devices, MP20, MP30, MP40, MP50 interfacing the Gas Analyzer model M1013A EGM (K041956). The modified devices have the same technological characteristics as the legally marketed predicate devices.

Here's a breakdown of the acceptance criteria and study information for the Philips IntelliVue Patient Monitor, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state specific quantitative acceptance criteria (e.g., "accuracy must be >95%") or detailed reported device performance in a numerical format for individual parameters. Instead, it makes a general statement about meeting specifications and reliability.

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "system level tests, performance tests, and safety testing from hazard analysis" but doesn't quantify the number of test cases, patient data, or specific scenarios used.
• Data Provenance: Not explicitly stated. It's unclear if the testing involved real patient data, simulated data, or a combination, nor the country of origin. The regulatory submission is from Germany, suggesting testing might have occurred there or within Phillips' global network. It is implied to be prospective testing, as it's for a new software release.

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

Not mentioned in the document. The type of device (patient monitor) suggests that ground truth would likely be based on established physiological measurement standards and potentially clinical expert review, but this is not detailed.

4. Adjudication Method for the Test Set

Not mentioned in the document.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

No, an MRMC comparative effectiveness study is not applicable and was not performed. This device is a physiological patient monitor, not an AI-assisted diagnostic tool for human readers. Its primary function is to measure and display physiological parameters, and alarm if they fall outside set limits. There's no AI component for improving human interpretation in the way one would see in an imaging AI application.

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

Yes, the testing described appears to be a standalone evaluation of the device's performance, functionality, and reliability, without explicitly involving human-in-the-loop performance for its core functions. The software Release B.1.1 itself is the "algorithm only" component that was tested.

7. The Type of Ground Truth Used

The document does not specify the exact "type of ground truth" in the way one might for an imaging algorithm (e.g., pathology, outcomes data). For a patient monitor, ground truth would be established through a combination of:

• Known physical inputs/simulations: Using calibration equipment or simulators to generate precise physiological signals (e.g., a known ECG rhythm, a specific blood pressure waveform) and verifying the device's output against these known inputs.
• Comparison to reference devices: Comparing the monitor's readings to those of other validated and highly accurate reference monitoring devices.
• Technical specifications: Verifying that the device's measurements fall within the specified accuracy and precision limits for each parameter.

8. The Sample Size for the Training Set

Not applicable. This device is a patient monitor, not an AI/machine learning algorithm that requires a "training set" in the conventional sense. The "training" would involve the development and engineering process of the software itself, not a data-driven training pipeline for a predictive model.

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

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

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