(25 days)
Indicated for use by health care professionals whenever there is a need for monitoring the physiological paramenters of patients.
The Philips MP2, X2, MP5, MP5T, MP20, MP30, MP40, MP50, MP60, MP70, MP75, MP80, and MP90 IntelliVue patient monitors are 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, MP5, MP5T, MP20, MP30, MP40, and MP50 are additionally intended for use in transport situations within hospital environments. The MP2, X2 and MP5 are also intended for use during patient transport outside of a hospital environment. The monitors are not intended for home use. They are intended for use by health care professionals.
This 510(k) summary describes a software revision (H.00) for Philips IntelliVue patient monitors, introducing a new model (MP75) and maintaining substantial equivalence to previously cleared devices. The submission focuses on verification and validation testing to ensure the modified devices meet performance and reliability requirements.
Here's an analysis based on your requested information:
1. Table of Acceptance Criteria and Reported Device Performance
The provided text does not include a specific table of acceptance criteria or detailed reported device performance metrics. Instead, it states broadly:
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Specifications cleared for predicate devices and hazard analysis. | "Test results showed substantial equivalence." "The results demonstrate that the Philips IntelliVue patient monitors meet all reliability requirements and performance claims." |
2. Sample Size Used for the Test Set and Data Provenance
The document states: "Testing involved system level and regression tests as well as testing from the hazard analysis."
- Sample Size: Not explicitly stated. The testing appears to be based on the general system and regression tests covering the IntelliVue patient monitor family and the new MP75 model.
- Data Provenance: Not specified. It's likely internal testing data generated by Philips. The document does not indicate whether it was retrospective or prospective, or the country of origin of the data.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and their Qualifications
Not applicable. The submission is for a software update to patient monitoring devices and does not involve AI/ML or expert adjudication for ground truth related to medical image interpretation or diagnosis. The "ground truth" here would be the expected functional performance and safety parameters of the monitoring system.
4. Adjudication Method for the Test Set
Not applicable. As noted above, this is not an AI/ML diagnostic device requiring expert adjudication. The "pass/fail criteria" were based on the specifications of predicate devices.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No. An MRMC study is not relevant for this type of device (patient physiological monitors with a software update). The submission is about maintaining performance equivalence, not evaluating human reader improvement with AI assistance.
6. Standalone (i.e. algorithm only without human-in-the-loop performance) Study
The assessment is inherently standalone in the sense that the device (monitor with software) is evaluated for its performance. The "algorithm" here is the monitoring software, and its performance is assessed against established specifications. There isn't a separate "algorithm-only" vs. "human-in-the-loop" concept described, as the device's function is to monitor and present physiological parameters to human users.
7. Type of Ground Truth Used
The ground truth used for testing appears to be the established performance specifications and safety requirements for patient physiological monitors, as defined by prior device clearances and hazard analyses. This would include parameters like accuracy of physiological measurements (e.g., heart rate, blood pressure, ECG), alarm functionality, and system reliability, rather than a diagnostic 'truth' from pathology or outcomes.
8. Sample Size for the Training Set
Not applicable. This is not an AI/ML device that requires a training set in the conventional sense of machine learning. The software development and testing follow traditional software engineering and medical device validation processes.
9. How the Ground Truth for the Training Set Was Established
Not applicable, as there is no mention of a training set for an AI/ML algorithm.
§ 870.1025 Arrhythmia detector and alarm (including ST-segment measurement and alarm).
(a)
Identification. The arrhythmia detector and alarm device monitors an electrocardiogram and is designed to produce a visible or audible signal or alarm when atrial or ventricular arrhythmia, such as premature contraction or ventricular fibrillation, occurs.(b)
Classification. Class II (special controls). The guidance document entitled “Class II Special Controls Guidance Document: Arrhythmia Detector and Alarm” will serve as the special control. See § 870.1 for the availability of this guidance document.