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Avalon Fetal Monitor FM30: Indicated for use by trained health care professionals whenever there is a need for monitoring of the physiological parameters uterine activity, heart rate, ECG, oxygen saturation, non-invasive blood pressure, and pulse rate of pregnant women and the fetal heart rate in labor and delivery rooms and in antepartum testing areas.

Avalon Fetal Monitor FM20: Indicated for use by trained health care professionals whenever there is a need for monitoring of the physiological parameters uterine activity, heart rate, non-invasive blood pressure, pulse rate of pregnant women and the fetal heart rate in labor and delivery rooms and in antepartum testing areas.

The subject devices Philips Avalon Fetal Monitors FM20 and FM30 are modification of the legally marketed Philips Series 50XM (M1350B) Fetal/Maternal Monitor (K954351) and offer monitoring of fetal and maternal heart rates, uterine activity, maternal ECG, maternal non-invasive blood pressure (NIBP) and oxygen saturation (Sp02) during antepartum testing and labor and delivery. The Avalon FM20 and FM30 are intended for non-invasive and invasive monitoring of the physiological parameters of pregnant women during antepartum testing and labor and delivery. The Avalon FM20 and FM30 are intended for monitoring fetal and maternal heart rates, uterine activity, maternal non-invasive blood pressure (NIBP) and oxygen saturation (SpO2). The Avalon FM20 and FM30 are intended for generating alarms, for displaying, storing and recording patient data and related waves.

Here's an analysis of the provided text regarding acceptance criteria and the study that proves the device meets them:

1. A table of acceptance criteria and the reported device performance

The provided text does not explicitly list specific quantitative acceptance criteria or detailed performance metrics. Instead, it makes a general statement:

Explanation: The submission is for a modified device (Philips Avalon Fetal Monitors FM20 and FM30) that is stated to have the "same technological characteristics" and "same intended use" as legally marketed predicate devices. The "acceptance criteria" here are implicitly tied to demonstrating that the modified device performs equivalently to the predicate devices and meets their established specifications. The text confirms that this equivalence was met.

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. The submission describes verification, validation, and testing activities against predicate device specifications, not a clinical study involving experts establishing ground truth for diagnostic accuracy.

4. Adjudication method for the test set

Not applicable. The submission describes engineering and system-level testing, not a clinical study requiring adjudication of expert interpretations.

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 is not an AI-powered diagnostic device, and no MRMC comparative effectiveness study is mentioned. The device is a fetal monitor.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

The device itself is a standalone fetal monitor designed to provide physiological data. Its "standalone" performance was assessed through system-level, performance, and safety tests against predicate device specifications. There is no "algorithm only" performance reported in the context of AI.

7. The type of ground truth used

The "ground truth" for the device's performance was established by the specifications cleared for the predicate device. The testing demonstrated that the modified device met these existing specifications.

8. The sample size for the training set

Not applicable. This is not a machine learning or AI device that requires a training set.

9. How the ground truth for the training set was established

Not applicable, as there is no training set for this type of device.

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