(30 days)
Intended for monitoring, recording, and alarming of multiple physiological parameters. For use in healthcare facilities by healthcare professionals whenever there is a need for monitoring the physiological parameters of adult, pediatric, or neonatal patients.
Philips M1175A/76A/77A Component Monitoring System ● Philips V24/V26 patient monitor .
The provided document is a 510(k) summary for the Philips M1175A/76A/77A Component Monitoring System and Philips V24/V26 Patient Monitor. It focuses on demonstrating substantial equivalence to previously cleared devices rather than presenting a standalone study with detailed acceptance criteria and performance metrics for a new AI/ML device.
Therefore, many of the requested sections regarding acceptance criteria, specific performance metrics, sample sizes for test and training sets, expert involvement, and ground truth establishment, which are typical for studies validating AI/ML-driven devices, are not present in this document.
However, based on the information provided, here's what can be extracted and inferred:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state acceptance criteria in a quantitative table format for performance metrics. Instead, it relies on demonstrating substantial equivalence to predicate devices. The "performance" reported is that the device "meets all reliability requirements and performance claims" and that "test results showed substantial equivalence."
| Acceptance Criteria (Inferred from substantial equivalence) | Reported Device Performance |
|---|---|
| Device performance and reliability are equivalent to predicate devices (K003038, K001333, K990125, K981576, K971910, and K903771) | Device "meet[s] all reliability requirements and performance claims." "Test results showed substantial equivalence." |
| Functionality in monitoring, recording, and alarming multiple physiological parameters is equivalent to predicate devices. | Device "has the same intended use as the legally marketed predicate devices." |
| Technological characteristics are equivalent to predicate devices. | Device "has the same technological characteristics as the legally marketed predicate devices." |
| Compliance with relevant specifications (e.g., AAMI SP-10 for blood pressure validation). | Modification provides a choice of validation references according to subclause 4.4.2.1 of AAMI SP-10 for pediatric and adult patients. |
2. Sample sizes used for the test set and the data provenance
The document does not specify a distinct "test set" sample size or data provenance in the context of evaluating a new algorithm or AI performance. The testing described involves "system level tests, integration tests, and safety testing from hazard analysis." These are likely internal verification and validation activities rather than a clinical study with a specific patient cohort.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. The document does not describe the establishment of ground truth by experts for a test set, as it is a 510(k) submission for substantial equivalence based on hardware and software modifications, not an AI/ML diagnostic or prognostic device requiring expert-adjudicated ground truth.
4. Adjudication method for the test set
Not applicable, as no external expert-adjudicated test set is described.
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
Not applicable. This submission predates widespread AI/ML integration into medical devices requiring MRMC studies to demonstrate human-AI collaboration benefits. The device monitors physiological parameters, and there is no mention of "human readers" or AI assistance in interpretation.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This is not an AI/ML algorithm in the modern sense. The "algorithm" is part of the overall monitoring system, and its performance is evaluated as part of the total system functionality and reliability. The document does not isolate "algorithm-only" performance from the device's integrated operation.
7. The type of ground truth used
The concept of "ground truth" as typically applied to AI/ML (e.g., pathology, clinical outcomes) is not explicitly discussed. The "truth" for this device's performance would be derived from:
- Predicate device specifications: The new device's performance is compared against the established performance and specifications of the legally marketed predicate devices.
- Industry standards: Compliance with standards like AAMI SP-10 for blood pressure validation.
- Internal specifications: Meeting the manufacturer's own design and reliability requirements.
8. The sample size for the training set
Not applicable. This is not an AI/ML device that requires a training set.
9. How the ground truth for the training set was established
Not applicable, as no training set is described.
§ 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.