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The Comfort Twin Nasal Mask is an accessory to a non-continuous ventilator (respirator), intended for use by adult patients prescribed continuous positive airway pressure (CPAP) or bi-level therapy in hospital, clinic and home environments.

The Comfort Twin Nasal mask is a respiratory nasal mask using a dual cushion design with built-in bellows and an inner sealing flap for improving unintentional leak. It is a single patient use accessory for use with CPAP or bi-level devices. The Comfort Twin mask is strapped to the patient's face covering the nose, and connected to tubing to a CPAP or bi-level flow generator. Positive pressure ventilation is then able to be applied to the lungs in a non-invasive way.

The provided text describes a 510(k) summary for the Comfort Twin Nasal Mask, which is a new device. The acceptance criteria and the study proving the device meets them are discussed in the context of demonstrating substantial equivalence to predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample Size Used for the Test Set and Data Provenance:

The document states, "The new device was validated using bench data." This indicates that the testing was performed in a laboratory setting. The specific sample size for the test set is not provided. The provenance of the data is bench data, implying controlled experimental conditions rather than human or animal studies.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:

Not applicable. Since the validation was performed using "bench data," there was no need for human experts to establish ground truth in the way it would be required for clinical image analysis or diagnostic studies. The ground truth would be defined by the technical specifications and measurements of the device components and its interaction with test equipment.

4. Adjudication Method for the Test Set:

Not applicable. As the validation was based on "bench data," an adjudication method for reconciling expert opinions is not relevant. The "adjudication" would involve comparing measurements against predefined technical specifications.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

No. A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted. The study focused on demonstrating substantial equivalence through bench data, comparing the performance characteristics of the new device to predicate devices. There is no mention of human readers or AI assistance in this context.

6. Standalone Performance (Algorithm Only Without Human-in-the-Loop Performance):

Yes, in spirit, but not in the context of an algorithm. The study essentially evaluates the "standalone" performance of the physical device itself (the Comfort Twin Nasal Mask) in a controlled bench setting, without human intervention or interpretation that would typically be associated with an algorithm. It's an engineering performance evaluation, not an AI algorithm evaluation.

7. Type of Ground Truth Used:

The ground truth used was technical specifications and performance characteristics of the predicate devices (Respironics Reusable II Contour Nasal Mask and Resmed Mirage Activa), against which the new device's "bench data" was compared. The statement "All performance characteristics performed within specification and comparable to the cited device predicates" implies that the predicate devices' established performance benchmarks served as the ground truth for comparison.

8. Sample Size for the Training Set:

Not applicable. This device is a physical medical device (nasal mask), not an AI algorithm that requires a training set. The concept of a "training set" is not relevant to this type of product submission.

9. How the Ground Truth for the Training Set Was Established:

Not applicable. As a physical device, there is no training set in the context of machine learning.

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The Mirage Swift is intended for Multiple Patient Reuse by adult patients (>30 Kg) prescribed continuous positive airway pressure or bilevel therapy for use in home, hospitals or clinics.

The Mirage Swift is designed for adult patients for the delivery of non-invasive ventilatory support using continuous positive airway pressure or bi-level therapy. It is intended for multiple patient re-use and is minimally obtrusive to the user providing a high level of comfort, ease-ofuse and seal.

The provided document is a 510(k) summary for the ResMed Mirage Swift Nasal Mask, seeking clearance for "Multiple Patient Reuse" after initially being cleared as a single patient reuse device.

Based on the provided text, there is no specific performance study with acceptance criteria and a detailed report of device performance as one might expect for a new AI/software device. This document is for a physical medical device (a CPAP mask) and focuses on demonstrating substantial equivalence to predicate devices for an expanded indication for use (multiple patient reuse), not for establishing performance metrics of a novel diagnostic or therapeutic algorithm.

Therefore, many of the requested points regarding acceptance criteria, sample sizes for test/training sets, expert adjudication, MRMC studies, or standalone algorithm performance are not applicable or discoverable from this particular 510(k) summary.

Here's an attempt to answer the questions based only on the provided text:

1. Table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative acceptance criteria or a direct performance report in the context of a new diagnostic/therapeutic device. Instead, the "acceptance criterion" is implicitly demonstrating substantial equivalence to predicate devices that are already cleared for multiple patient reuse. The reported "performance" is that this substantial equivalence has been met.

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

Not applicable. This is not a study assessing diagnostic performance against a test set. The "performance data" mentioned would likely pertain to cleaning, disinfection, and material compatibility relevant to multiple patient reuse, rather than a clinical outcome study with a "test set" of patients.

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)

Not applicable. "Ground truth" in the context of expert consensus for a diagnostic test is not relevant here. The "ground truth" for multiple patient reuse would be established through testing cleaning efficacy, material degradation after disinfection cycles, and biocompatibility, typically performed in a lab setting rather than by clinical experts establishing a diagnosis.

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

Not applicable. There is no "test set" in the context of expert adjudication for this type of submission.

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 document is about a physical CPAP mask, not an AI or software-assisted diagnostic device. Therefore, no MRMC study or AI improvement metrics are relevant or present.

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

Not applicable. This is a physical medical device, not an algorithm.

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

The concept of "ground truth" as it applies to diagnostic algorithms is not relevant here. For the "Multiple Patient Reuse" claim, the "ground truth" would be objective evidence from laboratory testing demonstrating that the device can be effectively cleaned and disinfected according to a validated protocol without material degradation or compromising safety/performance, making it suitable for subsequent patient use. The document states that "Performance data and rationale are provided" to support this.

8. The sample size for the training set

Not applicable. This is a physical medical device, not an algorithm that requires a "training set."

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

Not applicable. As above, this is not an algorithm requiring a "training set" or "ground truth" in that context.

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