(44 days)
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, 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 Philips IntelliVue Patient Monitor family comprises the multi-parameter patient monitor models: MP2, X2, MP5, MP5T, MP20, MP30, MP40, MP50, MP60, MP70, MP80, MP90 and MX800 IntelliVue Patient Monitors that consist of display units including built-in or separate flat panel displays and central processing units (CPU) and physiological measurement modules. All monitors share the same system architecture and exactly the same software is executed on each monitor. The IntelliVue Patient Monitors measure multiple physiological parameters such as surface ECG, invasive and non-invasive pressure, etc., generate alarms, record physiological signals, store derived data, and communicate derived data and alarms to central stations via the IntelliVue Clinical Network. The subject modification is the introduction of software revision H.03 for the entire IntelliVue Patient Monitors family.
The provided text describes a 510(k) summary for Philips IntelliVue Patient Monitors and does not contain detailed information about specific acceptance criteria or an explicit study proving device performance against those criteria in the way typically expected for AI/ML device submissions. This submission predates the widespread use of AI/ML in medical devices and therefore lacks the detailed performance study information relevant to algorithms.
However, based on the general nature of a 510(k) submission for a non-AI/ML device, I can infer some aspects and highlight why other information requested is not present.
Inferred Acceptance Criteria and Reported Device Performance (based on common 510(k) practice for patient monitors):
The acceptance criteria for this type of device (patient monitors with software revisions) would generally revolve around demonstrating that the new software revision maintains the safety, effectiveness, and performance of the predicate devices. The study proving this would typically be a series of verification and validation tests.
| Acceptance Criteria Category (Inferred) | Reported Device Performance (from text) |
|---|---|
| System Level Performance | "demonstrate that the Philips IntelliVue Patient Monitors meet all reliability requirements and performance claims." |
| Functionality | "establish the performance, functionality, and reliability characteristics of the modified devices with respect to the predicate." |
| Reliability | "establish the performance, functionality, and reliability characteristics of the modified devices with respect to the predicate." |
| Safety (Hazard Analysis) | "Testing involved system level and regression tests as well as testing from the hazard analysis." |
| Substantial Equivalence | "Test results showed substantial equivalence." |
| Alarm Functionality | (Implied by device description: "generate alarms for multiple physiological parameters") |
| Physiological Parameter Accuracy | (Implied by device description: "measure multiple physiological parameters") |
Information Not Available in the Provided Text (and Explanations):
-
Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
- Explanation: This type of detail is not typically provided in a high-level 510(k) summary for a patient monitor software update. The "test set" here refers to the actual testing environment (e.g., simulators, human subjects, recorded physiological data), but specific numbers, provenance, or retrospective/prospective nature of data for such testing are not disclosed.
-
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):
- Explanation: For a patient monitor, "ground truth" for physiological parameters is often established through calibrated reference devices or established physiological models, rather than expert human interpretation in the way it is for image analysis in AI/ML. Therefore, the concept of "experts" in this context is not directly applicable.
-
Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Explanation: Adjudication methods are relevant for subjective interpretations, especially in AI/ML where multiple experts might disagree. For physiological monitoring, accuracy is typically measured against objective numerical standards from reference devices, so adjudication amongst experts wouldn't be part of the testing.
-
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:
- Explanation: No. This is not an AI/ML device. MRMC studies are specific to evaluating how AI assistance impacts human performance, which is not applicable here.
-
If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Explanation: While the device's algorithms for physiological parameter measurement and alarm generation would have been tested in a standalone manner (e.g., against simulated signals or reference data), the term "standalone performance" is most commonly used in the context of AI/ML for its direct diagnostic or classification output. The submission does not detail specific standalone performance metrics for individual algorithms within the monitor.
-
The type of ground truth used (expert consensus, pathology, outcomes data, etc):
- Explanation: The "ground truth" for a patient monitor would be established using:
- Reference Devices: Calibrated instruments for measuring ECG, blood pressure, SpO2, etc., against which the monitor's readings are compared.
- Simulators: Devices that generate known physiological waveforms and values.
- Clinical Data (less likely for initial software change, more for initial device clearance): Real patient data where physiological parameters are independently measured by gold-standard methods.
- The document does not explicitly state which methods were used, but these are standard for patient monitor testing.
- Explanation: The "ground truth" for a patient monitor would be established using:
-
The sample size for the training set:
- Explanation: This device is not an AI/ML device, so there is no "training set" in the machine learning sense. The software revision is based on traditional programming and engineering principles, not statistical learning from a dataset.
-
How the ground truth for the training set was established:
- Explanation: As there is no training set for an AI/ML model, this question is not applicable.
§ 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.