(23 days)
Indications for Use (M3046A): For monitoring, recording and alarming of multiple physiological parameters of adults, pediatrics and neonates in hospital and/or medical transport environments.
Indications for Use (MP40/50/60/70/90) : Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters patients. Intended for monitoring, recording and alarming of multiple physiological parameters of adults, pediatrics and neonates in hospital environments.
The modification is the creation of a new measurement extension for the Philips patient monitor family (supported by the M3001A multi-measurement server).
The provided text describes a 510(k) submission for the "Hemodynamic Extension to the Multi-measurement Server - M3012A." However, the available information is very high-level and does not contain detailed acceptance criteria or results from a clinical study as typically found in comprehensive performance reports for devices like AI algorithms.
The document states that "Verification, validation, and testing activities were conducted to establish the performance and reliability characteristics of the new module using simulated systems." It mentions "system level tests, integration tests, environmental tests, safety testing from hazard analysis, interference testing, and hardware testing." The "Pass/Fail criteria were based on standards, where applicable, and on the specifications cleared for the predicate devices."
Based on the provided text, a detailed table of acceptance criteria and reported device performance (especially for a medical AI device), as well as many of the other requested details, cannot be extracted. This is because the submission refers to a hardware extension for a patient monitor and its performance testing focuses on system reliability and compliance with predicate device specifications, rather than a clinical effectiveness study of an AI algorithm's diagnostic or predictive performance.
Let's break down what can be answered based on the provided text, and what cannot:
1. A table of acceptance criteria and the reported device performance
The document mentions:
- "Pass/Fail criteria were based on standards, where applicable, and on the specifications cleared for the predicate devices."
- "The test results showed substantial equivalence. The results demonstrate that the Measurement Server Extension meets all the reliability requirements and performance claims."
Critical missing information: Specific performance metrics (e.g., accuracy, sensitivity, specificity for a diagnostic device), numerical thresholds for acceptance, or a detailed breakdown of what "all the reliability requirements and performance claims" entail. This document describes a hardware extension, not an AI model, so the type of performance metrics would be related to signal integrity, accuracy of measurements, reliability, etc., rather than the interpretative performance of an AI.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Critical missing information: The document states testing was conducted "using simulated systems," but no sample size for a test set (e.g., number of patients, cases, data points) or data provenance is provided.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Critical missing information: Since the testing was done on "simulated systems" and focuses on hardware reliability and not clinical interpretation, there is no mention of human experts establishing ground truth for a test set in the context of diagnostic or predictive performance.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Critical missing information: Not applicable to the type of testing 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
Critical missing information: Not applicable. This is not a study of an AI algorithm's impact on human performance.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
Critical missing information: Not applicable. This document is not describing an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Critical missing information: The "ground truth" for the tests described would be based on the known parameters of the "simulated systems" and reference measurements, rather than clinical ground truth like pathology or outcomes.
8. The sample size for the training set
Critical missing information: Not applicable, as this is not an AI algorithm requiring a training set.
9. How the ground truth for the training set was established
Critical missing information: Not applicable.
Summary Table of Extracted Information:
| Information Requested | Extracted Information / Status |
|---|---|
| 1. Acceptance Criteria & Reported Performance | Acceptance Criteria: "Pass/Fail criteria were based on standards, where applicable, and on the specifications cleared for the predicate devices." |
| Reported Performance: "The test results showed substantial equivalence. The results demonstrate that the Measurement Server Extension meets all the reliability requirements and performance claims." | |
| Critical Missing: Specific quantitative criteria and performance metrics. | |
| 2. Test Set Sample Size & Data Provenance | Sample Size: Not specified. Testing was "using simulated systems." |
| Data Provenance: Not applicable, as it involved simulated systems, not clinical data from specific countries or retrospective/prospective studies. | |
| 3. Number & Qualifications of Experts for Ground Truth | Not applicable. Testing involved simulated systems, not human interpretation requiring expert ground truth for clinical diagnostic/predictive performance. |
| 4. Adjudication Method for Test Set | Not applicable. |
| 5. MRMC Comparative Effectiveness Study (AI impact on humans) | Not applicable. This notification is for a hardware extension to a patient monitor, not an AI algorithm. |
| 6. Standalone Algorithm Performance | Not applicable. This notification is for a hardware extension. |
| 7. Type of Ground Truth Used | Ground truth would be based on known parameters of the simulated systems and reference measurements / engineering specifications. |
| Critical Missing: Specific details beyond "simulated systems." | |
| 8. Training Set Sample Size | Not applicable. This is for a hardware extension, not an AI algorithm. |
| 9. Ground Truth Establishment for Training Set | Not applicable. |
In conclusion, the provided 510(k) summary is for a hardware component with its associated verification and validation, not an AI-powered diagnostic or predictive device. Therefore, most of the requested details concerning clinical studies, expert-adjudicated ground truth, and AI performance metrics are not present in this document.
§ 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.