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The MiniMe™ Pediatric Mask is intended to provide an interface for application of CPAP or bi-level therapy. It is intended for patients > 2 years old and < 12 years old. The MiniMe Pediatric Mask is intended for single patient, multi-use in the home environment and multiple patients, multi-use in the hospital/ institutional environment.

The SleepNet MiniMe™ Pediatric Mask with vent holes for a fixed leak during use and without vent holes when used with a patient circuit that incorporates its own anti-asphyxia valve, It is single patient, multi-use or multi-patient, reusable.

Here's an analysis of the provided text regarding the MiniMe™ Pediatric Mask, structured to address your specific points about acceptance criteria and the supporting study:

This 510(k) submission primarily focuses on demonstrating substantial equivalence to predicate devices for a pediatric mask, which is a fairly low-risk device. Therefore, the "acceptance criteria" and "study" described are focused on comparative performance to established devices rather than demonstrating novel efficacy or specific clinical outcomes. The provided text doesn't contain a detailed clinical trial with a traditional "test set" or specific "ground truth" as one might expect for a diagnostic AI. Instead, it relies on engineering testing and comparison to predicates.

Acceptance Criteria and Reported Device Performance

The primary "acceptance criterion" for this 510(k) was substantial equivalence to the identified predicate devices in terms of indications for use, technology, materials, environment of use, and functional performance. The reported device performance met this criterion by demonstrating identity or similarity across these aspects and showing comparable engineering test results.

Study Information

Given the nature of the device (a mask accessory) and the 510(k) pathway, the "study" described is not a clinical trial but rather comparative performance testing against predicate devices.

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not explicitly stated. The type of testing (Pressure vs. Flow / leak, Internal volume / dead space) typically involves testing multiple units of the device and comparing them to multiple units of predicate devices. However, the exact number of units or data points is not provided.
   • Data Provenance: Not explicitly stated, but assumed to be from internal testing conducted by SleepNet Corporation, likely in a laboratory setting. There is no mention of country of origin for data or whether it's retrospective or prospective, as it's not a clinical study on patients.

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

   • Ground Truth Experts: Not applicable in the context of this engineering-focused comparative study. The "ground truth" for pressure, flow, leak, and dead space metrics would be established by validated measurement equipment and engineering standards, not human expert consensus for a "test set." The predicates themselves serve as the "ground truth" standard to be met.

3. Adjudication method for the test set:

   • Adjudication Method: Not applicable. This was engineering testing, not a subjective assessment requiring human adjudication. The results would be quantitative measurements.

4. 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:

   • MRMC Study: No, an MRMC study was not done. This device is a physical medical accessory (a mask), not an AI-powered diagnostic tool or image analysis system. Therefore, the concept of "human readers" and "AI assistance" is not relevant here.

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

   • Standalone Performance: Not applicable. This device is a physical medical accessory, not an algorithm.

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

   • The "ground truth" relied upon here is the established performance characteristics of the predicate devices as measured by engineering specifications (e.g., pressure-flow curves, leak rates, dead space volumes). The goal was to demonstrate that the new device's engineering performance for these metrics was "substantially equivalent" to these known and accepted predicates.

7. The sample size for the training set:

   • Training Set Sample Size: Not applicable. This device is not an AI algorithm requiring a training set. The "design" of the device would be based on engineering principles and potentially prior product experience, not a "training set" in the machine learning sense.

8. How the ground truth for the training set was established:

   • Ground Truth for Training Set: Not applicable, as there is no training set for this type of device.

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