(21 days)
Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients.
Intended use: For monitoring, recording and alarming of multiple physiological parameters of adults, pediatrics and neonates in healthcare environments. Additionally, the monitor may be used in transport situations within a healthcare facility.
The Philips M3046B Compact Portable Patient Monitors. The modification is the introduction of Release B.00 software for the Philips M3046B Compact Portable Patient Monitors and Accessories.
The provided document is a 510(k) summary for the Philips M3046B Compact Portable Patient Monitors, detailing a software modification (Release B.00). It focuses on demonstrating substantial equivalence to previously cleared devices rather than presenting a detailed study with specific acceptance criteria and performance metrics for a novel AI-powered device.
Therefore, much of the requested information regarding acceptance criteria, specific performance metrics, sample sizes for test/training sets, expert involvement, and ground truth establishment, which are typical for studies validating AI/ML medical devices, is not present in this document.
Here's an attempt to answer the questions based only on the provided text, highlighting where information is unavailable.
1. Table of Acceptance Criteria and Reported Device Performance
The document does not provide a table with specific, quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy targets) or corresponding reported device performance values for the modified software. It generally states that "Pass/Fail criteria were based on the specifications cleared for the predicate device" and that "Test results showed substantial equivalence."
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified quantitatively in the document. Pass/Fail criteria were based on specifications of predicate devices. | "Test results showed substantial equivalence." "The results demonstrate that the Philips M3046B Compact Portable Patient Monitors meets all reliability requirements and performance claims." |
2. Sample Size Used for the Test Set and Data Provenance
This information is not provided in the document. The document mentions "system level tests, performance tests, and safety testing from hazard analysis" but does not specify the sample sizes (e.g., number of patients, number of cases, length of recordings) used for these tests, nor the data provenance (country of origin, retrospective/prospective nature).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This information is not provided in the document. As this is a software update for a physiological monitor, "ground truth" would likely refer to the accuracy of physiological parameter measurements (e.g., ECG, NIBP, SpO2) against reference standards or expert interpretation of waveforms/events. However, no details on panel of experts or their qualifications are mentioned.
4. Adjudication Method for the Test Set
This information is not provided 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
This information is not provided and is irrelevant to this submission. The device is a patient monitor, not an AI-assisted diagnostic tool that requires human reader interpretation. The purpose of the submission is to demonstrate the safety and effectiveness of a software update for a physiological monitor, not to evaluate human performance with or without AI.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
The document describes "system level tests, performance tests, and safety testing," which would imply standalone performance evaluation of the device's functions. However, specific details about such testing are not provided, and the term "standalone" as typically used in AI/ML performance studies isn't explicitly used here. The context implies that the device's algorithmic performance (e.g., for parameter measurement, arrhythmia detection) was evaluated against established specifications of predicate devices.
7. The Type of Ground Truth Used
The document implies that "Pass/Fail criteria were based on the specifications cleared for the predicate device." For a physiological monitor, "ground truth" would typically involve:
- Reference Devices/Standards: Comparing measurements (e.g., blood pressure, SpO2, ECG heart rate) against highly accurate reference devices.
- Known Physiological Events: Testing the ability to correctly detect and alarm for known physiological events (e.g., arrhythmias).
- Simulated Data: Using standardized clinical scenarios or simulated waveforms.
The document does not explicitly state the specific type of ground truth used, but given the nature of the device, it would align with these types.
8. The Sample Size for the Training Set
This information is not provided in the document. As this is a software update for an existing physiological monitor, it's unlikely that the software involves a machine learning model that requires a "training set" in the sense of modern deep learning. The software is likely based on fixed algorithms or rule-based systems.
9. How the Ground Truth for the Training Set Was Established
This information is not provided and is likely not applicable, as explained in point 8.
§ 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.