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The Narval Brux is intended for protection of teeth and restorations from the forces of bruxism.

The Narval Brux is a removable intraoral device used for protecting teeth and restorations aqainst the forces of bruxism. It consists of patient-specific splints that fit separately over the upper or lower teeth. The design of the splints is dependent on the patient needs.

The device functions as a protective barrier for teeth and restorations by creating physical separation between posterior occlusal surfaces and/or anterior incisal edges which will prevent tooth damage caused by grinding, bruxing and may help alleviate jaw and muscle pain.

The device is customized for each patient based on the clinician prescription.

The provided document is a 510(k) summary for the Narval Brux device, which is a mouthguard intended for the protection of teeth and restorations from bruxism.

The document does not describe a clinical study that proves the device meets specific acceptance criteria in the way a clinical trial for an AI/ML device would. Instead, this 510(k) submission establishes substantial equivalence to a predicate device (SomnoBrux Splint(s), K102909) through non-clinical testing.

Therefore, many of the requested categories for a study proving acceptance criteria cannot be directly answered from this document.

Here's an attempt to answer the questions based on the provided information, noting where information is not available due to the nature of the submission:

1. Table of Acceptance Criteria and Reported Device Performance

For the Narval Brux, the acceptance criteria are not presented as quantitative performance metrics (e.g., sensitivity, specificity) but rather as successful completion of non-clinical tests ensuring comparable safety and efficacy to a predicate device.

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

• Sample size for test set: Not applicable in the context of this 510(k) submission, as no human clinical test set (like an AI/ML model's test set) was used. The submission focuses on non-clinical engineering and material tests.
• Data Provenance: Not specified for the non-clinical tests. Non-clinical data typically refers to laboratory or bench testing.

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 AI/ML context, is not relevant for this type of non-clinical device submission. The "ground truth" here would be the validated performance of the materials and design against engineering standards.

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

• Not applicable. There was no test set requiring expert adjudication.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is relevant for AI/ML diagnostic or assistive technologies, not for a physical medical device like a mouthguard, especially when establishing substantial equivalence via non-clinical data.

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

• Not applicable. This is not an AI/ML algorithm.

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

• The "ground truth" or basis for evaluation in this submission stems from established engineering standards, material safety standards (ISO 10993 for biocompatibility), and performance test methodologies (static compression) for mouthguards. The substantial equivalence argument relies on the new device performing equivalently to the predicate device in these non-clinical aspects.

8. The sample size for the training set

• Not applicable. There is no AI/ML training set mentioned in this document.

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

• Not applicable. There is no AI/ML training set described.

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The Narval CC™ is intended to reduce or alleviate snoring and mild to moderate obstructive sleep apnea (OSA) in adults.

The Narval CC is a removable intraoral device used for treating snoring and sleep apnea. It consist of two custom fabricated splints that fit separately over the upper and lower teeth and engage by means of adjustable rods. The device functions as a mandibular repositioner, maintaining the lower jaw in a forward position during sleep. This mechanical protrusion acts to increase the patient's pharyngeal space, improving his/her ability to exchange air and reducing the tendency to snore. The device is custom made for each patient and has an adjustment mechanism enabling the amount of mandibular advancement to be set by the dentist or physician at the time of fitting.

The provided text is a 510(k) summary for the ResMed Narval CC device, which is an intraoral device for snoring and obstructive sleep apnea. This document focuses on demonstrating substantial equivalence to predicate devices rather than providing a standalone study with acceptance criteria and device performance metrics in the way one might find for a novel diagnostic algorithm.

Here's a breakdown of why many of your requested items cannot be found in this specific document:

• This is a 510(k) Pre-Market Notification, not a clinical trial report or a performance study for a novel algorithm. The goal of a 510(k) is to demonstrate that a new device is "substantially equivalent" to a legally marketed predicate device, meaning it is as safe and effective. It generally doesn't require new clinical studies with detailed performance metrics and acceptance criteria for the new device if sufficient evidence of equivalence to a predicate exists.
• The device is a physical intraoral device, not an "AI algorithm." Many of your questions are geared towards the evaluation of AI/ML-based diagnostic systems. This product is a custom-fabricated physical medical device.

Therefore, many of your specific questions related to AI algorithm performance evaluation, such as "acceptance criteria," "reported device performance" in a metric table, "sample size for test set," "data provenance," "number of experts for ground truth," "adjudication method," "MRMC comparative effectiveness study," "standalone algorithm performance," "type of ground truth," and "training set size/ground truth" are not applicable or not present in this type of regulatory submission for this type of device.

What can be extracted or inferred from the document:

The document primarily focuses on demonstrating substantial equivalence to predicate devices. This is the "study" in this context. The "acceptance criteria" here are met if the FDA agrees that the new device shares sufficient similarities with the predicate devices in terms of intended use, technological characteristics, and does not raise new questions of safety and effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria (Implicit for 510(k) Substantial Equivalence): The device must be demonstrated to be "substantially equivalent" to predicate devices. This is achieved by showing that it has:
  • Essentially the same intended use and user population.
  • Equivalent technological characteristics.
  • Does not raise new questions of safety and effectiveness.
  • Is at least as safe and effective as the predicate devices.
• Reported Device Performance: Instead of specific quantitative performance metrics, the "performance" is demonstrated by asserting similarity to already cleared devices. The document explicitly states: "it is at least as safe and effective as the predicative devices."

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

• Not applicable / Not provided. This document does not describe a clinical performance study with a distinct "test set" in the context of an AI algorithm or a novel performance claim. The "study" here is a comparison to predicate devices based on design and intended use.

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. There is no "ground truth" establishment in the sense of expert annotation for a diagnostic algorithm. The ground truth for the predicate devices' effectiveness would have been established historically through their own clearance processes or clinical use, which are not detailed here.

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

• Not applicable. No adjudication method for a test set is described.

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 device is not an AI-assisted diagnostic tool, and no MRMC study is mentioned.

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

• Not applicable. This isn't an algorithm.

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

• Not applicable for this device's evaluation. The evaluation for this device is based on demonstrating similarity to legally marketed devices, whose "ground truth" (i.e., their efficacy and safety) was established through their own regulatory processes (e.g., clinical evidence, long-term use data, or previous equivalence findings).

8. The sample size for the training set:

• Not applicable. This isn't an AI/ML device requiring a training set.

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

• Not applicable.

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