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The ReDe Mask is indicated for use by healthcare facility procedural areas and recovery rooms as an adjunct to monitor breathing in adult patients who are sedated for a diagnostic or therapeutic procedure. The ReDe Mask measures the time period between the current and previous exhalation and illuminates a colored light during the exhalation that reflects the interval of time between breaths. If the interval is less than 7.5 seconds, the green light illuminates during exhalation; if the interval is greater than 7.5 seconds, the yellow light illuminates during exhalation; and if the interval between breaths is 20 seconds or longer, the red light flashes continuously. The ReDe Mask is only to be used when supplemental oxygen is provided by the facemask. The ReDe Mask is not a standalone device and is only to be used as an adjunct to pulse oximetry.

The ReDe Mask is a breathing frequency monitor that provides a visual signal at each breath event (i.e., an inhalation followed by an exhalation). The proposed device consists of a temperature-sensing circuit attached externally to a standard face mask. This circuit continuously analyzes the temperature inside the face mask and determines when a breath event has occurred.

The ReDe Mask is designed to detect breathing events (cycles of inhalation followed by exhalation) by measuring temperature changes in the immediate vicinity of a patient's nose and/or mouth. Exhalations produce a temperature warming as expired air exits the mouth and nose, while inhalations result in a temperature cooling as ambient air and supplied oxygen enter the mask. The overall pattern is thus one of repeating periods of a warming and a cooling phase with every breath. The rate of warming and cooling, that is, the slope of the temperature change (degrees C per unit time) depends on the vigor with which the patient is breathing, which can range from very shallow breathing (small slope values) to vigorous breaths (large slope values). The ReDe Mask's breath detection algorithms are based on continuously measuring the warming and cooling slopes coupled with real-time analysis to determine the changeover point from negative slope (inhalation phase) to positive slope (exhalation phase). It is the detection of inflection points in the slope (negative to positive) that yield the elapsed time period between successive breaths. With each new breath the elapsed time between inflection points is used to determine which LED to illuminate.

This document focuses on the ReDe Mask, a breathing frequency monitor. Here's a breakdown of the requested information based on the provided text:

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

The document does not explicitly state "acceptance criteria" in a tabular format with specific numerical targets. However, the various bench, biocompatibility, and clinical tests imply performance requirements. I will derive implied acceptance criteria and reported performance from the text.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Human Factors and Usability Validation (Study 1): 30 anesthesia care providers. Provenance not explicitly stated (likely prospective, conducted for this submission).
• Human Factors and Usability Validation (Study 2): Complete spectrum of healthcare providers who may use the device. Provenance not explicitly stated (likely prospective, conducted for this submission), "conducted inside the intended environment of use."
• Comparison of Accuracy With Capnograph: 38 individuals (non-patient volunteers). Provenance not explicitly stated (likely prospective, conducted for this submission).
• Comparison of Accuracy With ExSpiron and Capnograph: 50 individuals (non-patient volunteers). Provenance not explicitly stated (likely prospective, conducted for this submission).

The document does not specify the country of origin for these studies, but they were conducted in support of an FDA 510(k) submission, suggesting they align with US regulatory standards.

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)

The ground truth for the clinical accuracy comparisons was established using objective measurement devices:

• Capnograph (end-tidal CO2 measurements): Described as the "gold standard in monitor the breathing rate of a patient."
• ExSpiron (bioimpedance measurements): Used in conjunction with the capnograph.

The human factors and usability studies involved "anesthesia care providers" and "the complete spectrum of health care providers who may use the device." Their "correct interpretation" and "overwhelming positive feedback" served as an evaluation of usability, rather than ground truth generation for medical conditions.

No specific number or qualifications of "experts" (e.g., radiologists) were mentioned for establishing ground truth in the context of identifying medical conditions, as the device is a breathing monitor rather than a diagnostic tool.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not describe an adjudication method like "2+1" or "3+1" that would typically be used for expert consensus on diagnostic imaging. Instead, the accuracy studies compared the ReDe Mask's performance directly against established measurement devices (capnograph, ExSpiron). For human factors studies, a "study monitor observed and recorded any instances of subject difficulty, mishandling and/or misuse." This suggests direct observation and recording rather than a consensus-based adjudication process.

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

No, an MRMC comparative effectiveness study was not done. The ReDe Mask is a breathing frequency monitor, not an AI-assisted diagnostic tool that would typically involve "human readers" or "AI assistance" in the context of interpreting medical images or data. The studies focused on its standalone performance and usability compared to established medical monitoring devices and user interfaces.

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

Yes, standalone performance was assessed in the "Comparison of Accuracy With Capnograph" and "Comparison of Accuracy With ExSpiron and Capnograph" studies. In these studies, the ReDe Mask's detection of exhalation and low respiratory rates was compared directly with the capnograph and ExSpiron, without human intervention in the ReDe Mask's core function of detecting breaths and illuminating lights. The human involvement was in observing these lights and verifying their accuracy against the reference devices.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The ground truth used for the accuracy studies was based on objective measurements from:

• Capnograph: Considered the "gold standard in monitor the breathing rate of a patient," measuring end-tidal CO2.
• ExSpiron: A bioimpedance measurement device.

8. The sample size for the training set

The document does not provide information on a "training set" or "validation set" in the context of machine learning, as the ReDe Mask's breath detection relies on a temperature-sensing circuit and algorithms, rather than being explicitly described as an AI/ML device requiring significant training data. The "bench testing" and "clinical testing" described are for verification and validation of the device's performance against its specifications and predicate devices.

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

As no specific "training set" is mentioned for an AI/ML model, the establishment of ground truth for such a set is not applicable based on the provided text. The device's breath detection algorithms are based on "continuously measuring the warming and cooling slopes coupled with real-time analysis to determine the changeover point from negative slope (inhalation phase) to positive slope (exhalation phase)." This suggests a rule-based or signal processing approach rather than data-driven machine learning that would require a distinct training set with ground truth labels.

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