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The iNAP One Sleep Therapy System is indicated for home use in the treatment of obstructive sleep apnea (OSA) in adults in whom positive airway pressure is not the preferred treatment choice.

The iNAP One Sleep Therapy System consists of six (6) main components. The components include a console (Model R07-A), a saliva container, a saliva absorbent (iNAP DryPad), a flexible polymer tubing (iNAP Tubing Set), soft polymer oral interfaces (iNAP Oral Interface), a Muffler (Optional) and a software application for mobile devices (iNAP Lab+). One additional accessory set (102C) is included, which is a combination of the iNAP Oral Interface and iNAP Tubing Set. The function of iNAP One Sleep Therapy System (Console Model: R07-A) is to develop a negative pressure gradient in user's oral cavity which can be adjusted between -20 to -90 mmHg (-27 to -122 cmH2O) via the App (iNAP Lab+) to achieve or maintain proper vacuum pressure for users.

Here's a breakdown of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the iNAP One Sleep Therapy System are primarily focused on its pressure setting range, accuracy, and clinical performance in treating Obstructive Sleep Apnea (OSA). The reported device performance is based on the clinical study described.

2. Sample Size for the Test Set and Data Provenance

• Sample Size for Test Set: 30 OSA patients were initially enrolled in the clinical study. A subgroup analysis was performed on 18 subjects who received iNAP treatment within the intended pressure range of -20mmHg to -90mmHg.
• Data Provenance: Not explicitly stated, but the submission is from Somnics, Inc., located in Taiwan, and the regulatory consultant is in San Juan Capistrano, CA, USA. The study is described as a "clinical study," which implies it was a prospective study conducted for the purpose of evaluating the device. The specific country of origin for the patient data is not mentioned.

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

The text states that Polysomnogram (PSG) was used to evaluate inclusion criteria (AHI > 5), baseline AHI, and post-treatment AHI after 28 days of therapy. While PSG is the gold standard for diagnosing OSA and establishing AHI, the document does not specify the number of experts (e.g., sleep specialists, polysomnographic technologists) used to interpret these PSG studies or their specific qualifications.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for establishing the ground truth from the PSG results. It can be inferred that the PSG interpretations likely followed standard clinical protocols for sleep studies, which inherently involve expert interpretation.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs without AI assistance

There is no mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study or any evaluation of human readers improving with AI assistance. The device is a "Sleep Therapy System" that generates negative pressure for OSA treatment, not an AI-assisted diagnostic tool for human readers.

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

The device itself is a physical therapy system, not an algorithm. Therefore, a "standalone algorithm only" performance evaluation, as might be done for an AI diagnostic tool, is not applicable in this context. The clinical study evaluates the performance of the device in use by patients.

7. The Type of Ground Truth Used

The ground truth for evaluating the device's efficacy was established using Polysomnogram (PSG), which is considered the gold standard for diagnosing and quantifying Obstructive Sleep Apnea (OSA). The key metric used was the Apnea-Hypopnea Index (AHI).

8. The Sample Size for the Training Set

The document does not mention the existence of a "training set" in the context of machine learning. The clinical study described is an evaluation of the device's performance after its development, not a study to train an algorithm.

9. How the Ground Truth for the Training Set was Established

As no training set (in the machine learning sense) is mentioned, this question is not applicable. The clinical study serves to demonstrate the safety and effectiveness of the device itself.

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The iNAP One Sleep Therapy System is indicated for home use in the treatment of obstructive sleep apnea (OSA) in adults in whom positive airway pressure is not the preferred treatment choice.

The iNAP One Sleep Therapy System consists of six (6) main components. The components are a console, a saliva container, a saliva absorbent (iNAP DryPad), a flexible polymer tubing (iNAP Tubing Set), a soft polymer oral interface (iNAP Oral Interface) and a software application for mobile devices (iNAP Care). One additional accessory is Oral Interface with Tubing, which is a combination of the Oral Interface and Tubing Set. The function of iNAP One Sleep Therapy System is developing a negative pressure gradient in the user's oral cavity, which is set as -40 mmHg.

Please find below the requested information about the acceptance criteria and study proving the device meets these criteria:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document mentions two randomized studies for clinical testing:

• One performed in Taiwan.
• The other, a multicenter international study with sites in Germany, Taiwan, and the United States.

The specific sample size (number of participants) for these clinical studies, nor whether they were retrospective or prospective, is not explicitly stated in the provided text. However, randomized studies are generally prospective.

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 provided text does not explicitly state the number of experts used or their specific qualifications for establishing ground truth in the clinical studies. The clinical studies likely used objective measures (e.g., polysomnography data for OSA severity, adverse event reports) rather than expert consensus on diagnostic images.

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

The document does not specify any adjudication method for the clinical test set data. Given the nature of OSA treatment effectiveness and adverse event reporting, it's more likely that predefined clinical endpoints and safety criteria were used rather than a consensus-based adjudication for subjective interpretations.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not performed as this device (iNAP One Sleep Therapy System) is a treatment device for Obstructive Sleep Apnea, not a diagnostic imaging device involving human readers or AI assistance in image interpretation. The study focused on the effectiveness of the physical device compared to a predicate device.

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

This question is not applicable as the iNAP One Sleep Therapy System is a physical medical device for treating OSA, not an algorithm, and it operates with a human user (the patient). The software component (iNAP Care mobile app) is for tracking usage records and sealing-leakage time ratio, not for standalone diagnostic or therapeutic action.

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

For the clinical studies, the ground truth would have been established through outcomes data, including:

• Measures of Obstructive Sleep Apnea severity (though specific metrics like AHI are not detailed in this summary, they are standard for OSA studies).
• Adverse event data for safety comparison.
• Patient compliance and usage data.

8. The sample size for the training set

The document does not mention a training set in the context of device performance evaluation. The clinical studies were randomized trials comparing the subject device to a predicate, not machine learning model training.

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

This question is not applicable as there is no mention of a training set for machine learning or AI algorithm development in the provided text.

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