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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, MP20, MP30, MP40, and MP50 are additionally intended for use in transport situations within hospital environments. The MP5 is 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. The modification is the introduction of the models MP2 and X2 IntelliVue Patient Monitors and the introduction of software release F.00 for the entire IntelliVue Patient Monitors family, models MP2, X2, MP5, MP20, MP30, MP40, MP50, MP60, MP70, MP80, and MP90.

The Philips IntelliVue Patient Monitors (models MP2, X2, MP5, MP20, MP30, MP40, MP50, MP60, MP70, MP80, and MP90, with software release F.00) are intended for monitoring and recording multiple physiological parameters and generating alarms for adults, pediatrics, and neonates in hospital environments. Some models (MP2, X2, MP20, MP30, MP40, MP50) are also for hospital transport, and the MP5 is for transport outside the hospital. They are intended for use by healthcare professionals.

Acceptance Criteria and Device Performance:

The provided 510(k) summary states that "Pass/Fail criteria were based on the specifications cleared for the predicate devices." However, it does not provide specific quantitative acceptance criteria or detailed reported device performance metrics in a table. It generally states that "test results showed substantial equivalence" and that the "results demonstrate that the Philips IntelliVue Patient Monitors meet all reliability requirements and performance claims."

Study Details:

1. Sample size for the test set and data provenance:
   The document does not specify the sample size for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It only mentions "system level and regression tests, safety and performance tests, EMC and environmental tests, such as testing from the hazard analysis."

2. Number of experts used to establish the ground truth for the test set and qualifications:
   This information is not provided in the document.

3. Adjudication method for the test set:
   This information is not provided in the document.

4. Multi-reader multi-case (MRMC) comparative effectiveness study:
   No MRMC comparative effectiveness study is mentioned. The submission focuses on demonstrating substantial equivalence to predicate devices through technical verification and validation, not comparative effectiveness with human readers.

5. Standalone (algorithm only without human-in-the-loop performance) study:
   The submission describes testing activities for the device itself ("Verification, validation, and testing activities establish the performance, functionality, and reliability characteristics of the modified devices"). This implies standalone performance testing of the device's functions, but details on specific standalone performance metrics or a study explicitly labeled as such are not provided. The device is a patient monitor, implying continuous monitoring of physiological parameters by the device itself before human interpretation.

6. Type of ground truth used:
   The document does not explicitly state the type of ground truth used for performance evaluation, beyond stating that "Pass/Fail criteria were based on the specifications cleared for the predicate devices." For physiological monitoring, ground truth would typically come from calibrated reference measurements or expert clinical assessment for phenomena like arrhythmia detection.

7. Sample size for the training set:
   This information is not provided. The document describes verification and validation activities for the device, but does not mention "training sets," which implies that this device might not incorporate machine learning or AI that requires a distinct training phase in the way a diagnostic imaging AI would.

8. How the ground truth for the training set was established:
   As no training set is explicitly mentioned or implied to be relevant to the device's development as described, this information is not provided.

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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 MP5 is also intended for use in transport situations within hospital environments.

The names of the device is the Philips MP5 IntelliVue Patient Monitor

The Philips MP5 IntelliVue Patient Monitor is a patient monitoring device. The provided text outlines the declaration of substantial equivalence for this device to previously cleared devices. It does not contain detailed information about specific acceptance criteria for performance metrics (like accuracy for heart rate, blood pressure, etc.) or a study that specifically proves the device meets those criteria with granular data and methodology.

However, based on the document, we can infer the approach taken:

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

The document states: "Pass/Fail criteria were based on the specifications cleared for the predicate device and test results showed substantial equivalence." This indicates that the acceptance criteria for the MP5 IntelliVue Patient Monitor were inherently tied to, and met, the established performance specifications of its predicate devices (Philips MP30 IntelliVue Patient Monitor and its modules).

Since specific numerical acceptance criteria and reported performance values for parameters like arrhythmia detection sensitivity/specificity, blood pressure accuracy, or oxygen saturation accuracy are not provided in this summary, a detailed table cannot be created from the given text. The general statement of "substantial equivalence" is the reported overall performance.

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

• Sample Size for Test Set: Not specified. The document mentions "system level tests, performance tests, and safety testing," but does not detail the number of patients, test cases, or data points used in these tests.
• Data Provenance: Not specified. There is no mention of the country of origin of the data or whether it was retrospective or prospective.

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

• Number of Experts: Not applicable/Not specified. This device is a physiological monitor, and its "ground truth" for parameters like heart rate, blood pressure, or oxygen saturation would typically be established by validated reference methods or other highly accurate medical equipment, not by human experts adjudicating diagnoses. The testing would compare the device's readings against these reference standards. If "safety testing from hazard analysis" involved expert review, it is not detailed.
• Qualifications of Experts: Not applicable/Not specified.

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

• Adjudication Method: Not applicable/Not specified. As mentioned above, "ground truth" for physiological parameters is typically established by reference instruments, not through expert adjudication in the same way as, for example, image interpretation.

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:

• MRMC Study: No. This device is a patient monitor, not an AI-assisted diagnostic tool that aids human readers. Therefore, an MRMC comparative effectiveness study comparing human readers with and without AI assistance is not relevant to this type of device and was not performed.
• Effect Size: Not applicable.

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

• Standalone Performance: The testing described ("system level tests, performance tests, and safety testing") would inherently evaluate the standalone performance of the device's algorithms and hardware in measuring and reporting physiological parameters. While human interaction is involved in setting up and monitoring, the core performance metrics (e.g., accuracy of readings, alarm thresholds) are assessed based on the device's output. However, the document does not break down the results into explicit "standalone algorithm performance" versus "human-in-the-loop performance."

7. The type of ground truth used:

• Type of Ground Truth: Inferred to be reference standard measurements. For physiological parameters measured by a patient monitor (e.g., ECG, blood pressure, SpO2, temperature, gas analysis), the "ground truth" would generally be established using highly accurate and calibrated reference devices or methodologies. For example, a "true" heart rate might be determined by a known ECG simulator, or blood pressure by an invasive catheter with a calibrated transducer.

8. The sample size for the training set:

• Sample Size for Training Set: Not applicable/Not specified. This document describes a traditional medical device (patient monitor) and its declaration of substantial equivalence, not a machine learning or AI-based device that typically undergoes a distinct "training" phase with a specific dataset.

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

• Ground Truth Establishment for Training Set: Not applicable/Not specified. As above, a "training set" and its associated ground truth are primarily concepts in machine learning/AI development, which is not the focus of this 510(k) summary.

In summary, the 510(k) pertains to a traditional patient monitor. The testing performed focused on verifying that the modified device met the established specifications of its predicate devices, thereby demonstrating "substantial equivalence." The details typically requested for AI/ML-based devices (like MRMC studies, expert adjudication, training set specifics) are not applicable or provided for this type of submission.

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