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The Sleepnet Mojo 2 Full Face Vented Mask, Veraseal 3 Full Face Vented Mask, and V3 Full Face Vented Mask are intended to be used with positive airway pressure devices, such as CPAP or bi-level, operating at or above 3 cm H₂O.

The masks are to be used on adult patients (>30 kg.) for whom positive airway pressure therapy has been prescribed.

Veraseal 3: Disposable single use (hospital/institutional), Single patient, multi-use up to 7 days (hospital/institutional)
V3: Single patient, multi-use (home)
Mojo 2: Single patient, multi-use (home or hospital/institutional)

The Sleepnet Mojo 2, Veraseal 3 and V3 Full Face mask are provided with an exhalation elbow and anti-asphyxia (AAV) valve.

The subject of this submission is:

• Slight redesign of the shape of the masks but no appreciable differences as the identical exhalation elbow and anti-asphyxia valve (AAV)
• Add an additional size, ex-large, mask to the product line
• Support useful life of the V3 and Mojo 2 up to 6 months as a single patient, multi-use mask

Veraseal 3 is intended as a Disposable single use (hospital/institutional) and as a Single patient, multi-use up to 7 days (hospital/institutional)
V3 is intended as a Single patient, multi-use (home)
Mojo 2 is intended as a Single patient, multi-use (home or hospital/institutional

The provided document, a 510(k) summary for certain full face vented masks, focuses on demonstrating substantial equivalence to predicate devices rather than detailing a study that proves the device meets specific acceptance criteria through novel performance data. The core of this submission is about showing that the new masks are as safe and effective as pre-existing, legally marketed devices.

Therefore, many of the requested details, such as sample sizes for test sets, number of experts for ground truth, MRMC studies, standalone algorithm performance, and training set information (which are typical for AI/ML device submissions or devices requiring extensive clinical performance testing), are not applicable to this 510(k) for a respiratory mask.

However, based on the principle of demonstrating substantial equivalence, we can infer the "acceptance criteria" and "device performance" relate to meeting the standards of the predicate device and relevant international standards.

Here's an interpretation of the available information in the context of the prompt:

Acceptance Criteria and Reported Device Performance

The document states that the performance of the new masks was "compared" to predicate devices and that the new masks "Meets ISO 17510 testing". This indicates that the acceptance criteria are adherence to established performance benchmarks (likely from ISO 17510) and performance comparable to the predicate device.

1. Table of Acceptance Criteria and the Reported Device Performance

Study Details (as inferable from a 510(k) for a medical device)

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

• Sample Size: Not explicitly stated as a number of patients or cases but rather as a series of bench tests on device samples. For physical device testing (like pressure drop, CO2 washout, mechanical drop), typically multiple units of the device are tested to ensure consistency and reliability. The specific number of units tested for each benchmark is not provided in this summary.
• Data Provenance: The data are generated from bench testing (laboratory-based performance evaluations) rather than clinical studies with patient data. The country of origin of the data is not specified but would be where the manufacturer (Sleepnet Corporation, Hampton, NH, USA) conducts its testing or where a contracted testing facility is located. The tests are prospective in the sense that they are conducted specifically for this submission to demonstrate performance.

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

• Not Applicable. For mechanical/performance bench testing of a device like a mask, "ground truth" is established through physical measurements and adherence to engineering specifications and international standards (like ISO 17510), rather than expert consensus on medical images or clinical outcomes. The "experts" would be the engineers and technicians performing and verifying the tests, ensuring compliance with established protocols and standards.

4. Adjudication method for the test set

• Not Applicable. Adjudication methods (like 2+1, 3+1) are typically used for establishing ground truth in clinical data (e.g., radiology reads) where subjective interpretation might vary. For bench testing, test results are typically objective measurements compared against predefined pass/fail criteria or reference values.

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 a physical medical device (mask), not an AI/ML diagnostic or assistive device that involves "human readers" or "AI assistance."

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

• Not Applicable. Same as above, not an AI/ML algorithm.

7. The type of ground truth used

• The "ground truth" for the bench tests is defined by engineering specifications, international standards (specifically ISO 17510), and the performance characteristics of the legally marketed predicate devices. For example, the acceptable range for CO2 washout is defined by ISO standards, and the performance of the predicate device serves as a comparative benchmark.

8. The sample size for the training set

• Not Applicable. This is not an AI/ML device that requires a "training set" for model development.

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

• Not Applicable. (See point 8).

In summary, this 510(k) relies heavily on demonstrating that the new mask designs maintain the same essential safety and performance characteristics as the previously cleared predicate device, and that they meet relevant international standards through various bench tests. The "study" here is a series of engineering and performance evaluations designed to prove this equivalence, rather than a clinical trial or AI model validation study.

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