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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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