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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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M3290A: For central monitoring of multiple adult, pediatric, and neonatal patients; and where the clinician decides to monitor cardiac arrhythmia of adult, pediatric, and neonatal patients and/or ST segment of adult patients to gain information for treatment, to monitor adequacy of treatment, or to exclude causes of symptoms.

M4840A: For ambulatory and bedside monitoring of ECG and SpO2 parameters of adult and pediatric patients in healthcare facilities to gain information for treatment, to monitor adequacy of treatment, or to exclude causes of symptoms.

The Philips M3290A IntelliVue Information Center Software Release F.0 and M4840A Philips Telemetry System II with M4841A patient device.

This Philips submission for the M3290A IntelliVue Information Center Software Release F.0 and M4840A Philips Telemetry System II with M4841A patient device describes modifications (changes in ECG chest lead support, NBP limit alarms, and network functionality) and asserts substantial equivalence to a previously cleared predicate device (K040357). The documentation does not contain a detailed study with specific acceptance criteria and device performance metrics, as would typically be presented for de novo device approval or significant design changes requiring new clinical validation.

Instead, the submission states that:

"Verification, validation, and testing activities have successfully established the performance, functionality, and reliability characteristics of the new devices with respect to the predicates. Testing involved system level tests, integration tests, environmental tests, and safety testing from hazard analysis. Pass/Fail criteria were based on the specifications cleared for the predicate devices and test results showed substantial equivalence. The results successfully demonstrate that patient monitoring system functionality meets all reliability requirements and performance claims and is substantially equivalent to the predicate devices."

This indicates that the modifications were evaluated against the established performance specifications of the predicate device, and the testing confirmed that the changes did not degrade performance below those existing benchmarks.

Therefore, not all questions can be directly answered as the provided text does not contain a discrete study with defined acceptance criteria and performance data for this specific submission's modifications. The information below reflects what can be extracted or inferred from the provided text regarding the evaluation approach.

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided text, specific quantitative acceptance criteria and detailed device performance metrics (true positive rate, false positive rate, sensitivity, specificity, accuracy, AUC, F1-score) are not explicitly stated for the modified device. The document states that "Pass/Fail criteria were based on the specifications cleared for the predicate devices and test results showed substantial equivalence." This implies that the device maintained the performance characteristics of its predicate.

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

• Test set sample size: Not specified. The testing described includes "system level tests, integration tests, environmental tests, and safety testing from hazard analysis," which are typically internal engineering and validation tests rather than clinical studies with patient data in the context of AI/ML evaluation.
• Data provenance: Not specified. Given the nature of the modifications (adding support for an ECG chest lead, NBP alarms, network functionality), the testing likely involved controlled testing environments and simulated data, potentially with some real physiological data from internal archives if applicable, but no geographically or retrospectively/prospectively defined clinical dataset is mentioned.

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

• Not applicable/Not specified. The evaluation focused on technical performance and substantial equivalence to a predicate device, not on diagnostic accuracy requiring expert ground truth labels for a dataset.

4. Adjudication method for the test set

• Not applicable/Not specified. There is no mention of a human adjudication process for establishing ground truth for a test set.

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

• No MRMC comparative effectiveness study was performed or mentioned. This submission does not describe an AI/ML algorithm intended to assist human readers.

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

• The device itself is a patient monitoring system, which includes automated capabilities like arrhythmia detection and ST segment monitoring. The testing described implicitly evaluates these "standalone" functionalities against their specifications, as part of assessing substantial equivalence to the predicate. However, specific performance metrics for individual algorithms (e.g., arrhythmia detection algorithm sensitivity) are not provided in this summary.

7. The type of ground truth used

• For the technical and safety testing conducted, the ground truth would be based on device specifications, engineering requirements, and regulatory standards. For example, in testing NBP alarms, the ground truth would be the defined NBP limits and whether the system correctly triggered an alarm when those limits were exceeded based on simulated or measured blood pressure values. For ECG lead support, the ground truth would be the accurate acquisition and display of the ECG signal via the new lead.

8. The sample size for the training set

• Not applicable. This device, as described in this 2004 submission, is a traditional medical device (patient monitor) with pre-specified algorithms, not a machine learning or AI device that undergoes a training phase with a distinct training set.

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

• Not applicable. As noted above, this is not an AI/ML device that requires a training set and associated ground truth establishment.

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Indicated for use by health care professionals monitoring the physiological parameters whenever there is a need for monitoring, recording and alarming of multiple physiological parameters of adults, pediatrics and neonates in hospital environment and during transport within hospital environments. Models MP20, MP30, MP40 and MP50 are additionally intended for use in transport situations within hospital environments. ST Segment monitoring is restricted to adult patients only. The transcutaneous gas measurement (tcp0₂ / tcpCO₂) is restricted to neonatal patients only.

The Philips MP20, MP30, MP40, MP50, MP60, MP70, and MP90 IntelliVue Patient Monitors.

The Philips MP20, MP30, MP40, MP50, MP60, MP70, and MP90 IntelliVue Patient Monitors underwent verification, validation, and testing activities to establish their performance, functionality, and reliability characteristics. The study that proves the device meets the acceptance criteria is described as follows:

1. Table of Acceptance Criteria and Reported Device Performance:

Note: The 510(k) summary explicitly states that "Pass/Fail criteria were based on the specifications cleared for the predicate device." However, the exact quantitative metrics for these specifications are not provided in the summary.

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

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It generally refers to "system level tests, performance tests, and safety testing."

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

This information is not provided in the 510(k) summary. The testing appears to be based on engineering and predefined specifications rather than expert adjudicated ground truth on clinical data.

4. Adjudication Method for the Test Set:

This information is not provided. The testing described appears to be based on objective pass/fail criteria derived from predicate device specifications, rather than a clinical adjudication process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness study was done:

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or performed. The submission focuses on substantial equivalence to predicate devices through technical testing.

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

The testing described appears to be standalone performance testing of the device's hardware and software against predefined specifications. The summary does not describe any human-in-the-loop performance evaluation in the context of this submission.

7. The type of Ground Truth Used:

The "ground truth" for the device's performance was based on the "specifications cleared for the predicate device." This implies a comparison to established technical and performance requirements of previously approved devices, rather than clinical outcomes data or pathology.

8. The Sample Size for the Training Set:

This information is not applicable or provided. The submission describes a medical device update (software release and new models) and does not suggest the use of machine learning algorithms that would require a distinct "training set" for model development.

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

This information is not applicable or provided, as the submission does not detail the use of a training set for machine learning. The device's performance validation is based on adherence to the specifications of predicate devices.

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