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The Luna® G3 BPAP 25A is a Bi-level Positive Airway Pressure) device designed for the treatment of adult Obstructive Sleep Apnea (OSA). The integrated humidification and warming of air from the flow generator device. These devices are intended for single-patient use by prescription in the home or hospital/ institutional environment on adult patients. If is to be used on patients >66lbs/30kg for whom CPAP therapy has been prescribed. The system can deliver bi-level therapy or auto bi-level therapy.

The Luna® G3 BPAP 25A is a microprocessor controlled, blower-based system that generates bi-level positive airway pressure (IPAP/EPAP) to support treatment of obstructive sleep apnea. The system provides fixed or auto-adjust pressure from 4 to 25 cmH2O above the ambient atmospheric pressure to a patient's oral/nasal airway.

This document, primarily a 510(k) Pre-market Notification to the FDA, describes a medical device called the Luna® G3 BPAP 25A, a Bi-level Positive Airway Pressure (BPAP) device for treating Obstructive Sleep Apnea (OSA). The document outlines its intended use, technological characteristics, and compares it to predicate devices to prove substantial equivalence.

Acceptance Criteria and Device Performance:

The acceptance criteria for the Luna® G3 BPAP 25A are not explicitly stated as quantitative performance metrics (e.g., accuracy, sensitivity, specificity) but are derived from comparing its technological characteristics and performance against legally marketed predicate and reference devices, as well as adherence to recognized regulatory standards. The "reported device performance" refers to the demonstrated compliance with these standards and the equivalence of its characteristics to established devices.

Here's a table summarizing the implicit acceptance criteria (based on predicate device characteristics and regulatory standards) and the reported performance of the subject device:

Study Details:

The document describes a submission for a 510(k) premarket notification, which relies on demonstrating substantial equivalence to existing legally marketed devices rather than conducting new clinical trials to prove efficacy from scratch. Therefore, the "study" is a collection of non-clinical tests and comparisons to predicate devices.

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

   • This document does not describe a clinical "test set" in the context of patient data. The evaluation is based on non-clinical performance data and comparison of device specifications.
   • For biocompatibility, electrical safety, EMC, software, and mechanical testing, specific sample sizes for components or devices under test are not provided. These are typically standard engineering and regulatory tests performed on a defined number of device units or materials.
   • The provenance of data for these non-clinical tests would be the manufacturer's testing facilities or certified third-party labs where the tests were performed. This is not explicitly stated but is implied by the submission from 3B Medical, Inc. (USA) and the manufacturer BMC Medical CO., LTD. (CHINA).

2. 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):

   • No "ground truth" established by external experts in medical diagnosis (like radiologists) is relevant for this type of 510(k) submission, as it focuses on engineering and performance equivalence, not a new clinical diagnostic claim.
   • The "experts" involved would be the engineers, toxicologists, and regulatory specialists who conducted and reviewed the non-clinical tests (biocompatibility, electrical safety, software validation, etc.) and assessed the substantial equivalence. Their specific qualifications are not detailed in this summary but are implicit in the adherence to international standards and FDA guidance.

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

   • There is no "adjudication method" in the sense of expert review of case data for this submission. The determination of substantial equivalence is made by the FDA based on the provided non-clinical data and comparisons.

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:

   • No MRMC comparative effectiveness study was done. This device is a therapeutic device (BPAP machine), not an AI-assisted diagnostic or interpretative tool that would involve human "readers."

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

   • The device has algorithms for controlling pressure delivery and detecting respiratory events. The document states that the blower and algorithm are the same as the primary predicate device for respiratory event detection and therapy. The AutoCPAP algorithm is identical to a reference device.
   • The performance of these algorithms is implicitly validated through the device's conformance to safety standards and its functional equivalence to predicate devices, which are already marketed and established as safe and effective. No specific "standalone" performance study of the algorithm in isolation (e.g., measuring its diagnostic accuracy against a gold standard in a clinical population) is described, as the 510(k) pathway focuses on equivalence.

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

   • There is no "ground truth" of clinical outcomes or expert consensus in this submission. The "ground truth" in this context refers to the established safety and performance characteristics of the predicate devices and the requirements of the relevant international and FDA standards (e.g., for biocompatibility, electrical safety, software V&V). The device demonstrates its performance by meeting these standards and showing that its specifications are equivalent or superior to the predicate devices without raising new safety or effectiveness concerns.

7. The sample size for the training set:

   • Not applicable. This device is not an AI/ML device that requires a "training set" in the sense of learning from clinical data. The algorithms are based on established principles of respiratory therapy.

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

   • Not applicable, as no training set is described.

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