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The Serena Sleep Night Guard/Occlusal Appliance is a removable medical device that is fitted to the patient's mouth and is intended to protect teeth and restorations against the forces of bruxism and alleviate temporomandibular joint, jaw, and muscle and tension headache pains.

The Serena Sleep Night Guard/Occlusal Appliance is a custom-made intraoral device used for protecting teeth and restorations against the forces of bruxism and alleviating temporomandibular joint, jaw, and muscle and tension headache pains.

The provided text is an FDA 510(k) summary for the Serena Sleep Night Guard, a medical device. It describes the device, its intended use, and its equivalence to a predicate device. However, it does not contain information about acceptance criteria or a study proving the device meets those criteria in the context of an AI/algorithm-based diagnostic or prognostic system.

The submission is for a physical medical device (a night guard), and the "performance testing" section refers to bench testing for physical properties like flexural strength, water sorption, and durability, and states that no clinical testing was deemed necessary due to the similarity to the predicate device.

Therefore, I cannot extract the information required to answer your prompt from the provided text, as it specifically asks for details related to AI/algorithm performance and clinical validation studies common in the context of AI-driven medical devices.

Here's why and what's missing:

• Acceptance Criteria for AI Performance: The document does not mention any AI, algorithms, or software intended for diagnostic or prognostic purposes. Thus, there are no acceptance criteria related to sensitivity, specificity, AUC, or other metrics typically used for assessing AI performance in a clinical setting.
• Study Proving Device Meets Criteria: Since there's no AI component, there's no study described that evaluates AI performance against clinical outcomes or expert labels. The document explicitly states "no Clinical Testing were deemed necessary."
• Sample Size, Data Provenance, Experts for Ground Truth, Adjudication, MRMC, Standalone Performance, Type of Ground Truth, Training Set Size/Ground Truth: All these points are relevant to the validation of AI/ML models in medical devices. As the Serena Sleep Night Guard is a physical device analogous to a 3D-printed appliance, these concepts do not apply to its regulatory submission as described.

In summary, the provided document is a 510(k) for a physical medical device and does not contain the information required to address your prompt about AI/algorithm acceptance criteria and validation studies.

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The Serena Sleep Appliance is a removable medical device that is fitted in the patient's mouth and is intended to reduce or alleviate snoring and mild to moderate obstructive sleep apnea (OSA) in adults.

The Serena Sleep Block Mandibular Advancement is an intraoral device that treats snoring and sleep apnea through mandibular repositioning the lower jaw forward from its normal location, the patient's pharyngeal space is increased and their ability to exchange air during sleep is improved. This device consists of custom fitted trays which fit over the upper and lower teeth. Based on the physician's prescription, positioning blocks are built into the upper and lower trays The mandibular advancement is achieved through the physician's selection of a specific upper and lower tray that when fitted, sets the optimal mandibular advancement. The separate two-piece construction is desirable for patients that want greater range of motion and lateral movement.

The Elastic Mandibular Advancement model is similar to the Block Mandibular Advancement but has exterior pins that allow for the use of rubber bands rather than the blocks. These bands provide additional treatment options and forces for repositioning the bottom jaw.

The provided text describes the 510(k) premarket notification for the Serena Sleep Block Mandibular Advancement (BMA) and Elastic Mandibular Advancement (EMA) devices. The core of the submission is to demonstrate substantial equivalence to legally marketed predicate devices, rather than proving a specific performance metric against acceptance criteria in the traditional sense of a new technology.

Therefore, the "acceptance criteria" here are based on the comparison to the predicate devices, showing that the Serena Sleep devices are as safe and effective as the existing ones. The "study" proving this largely involves bench testing and a detailed comparison of features.

Here's an analysis of the "acceptance criteria" and the "study" based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly met by demonstrating "equivalence" to the predicate devices across various specifications. The "reported device performance" is mainly shown through bench testing results.

2. Sample Size Used for the Test Set and Data Provenance

The provided document does not mention a "test set" in the context of human subjects or a clinical study. It refers to "Performance Testing (Bench)" which would involve physical device samples.

• Sample Size for Bench Testing: Not explicitly stated how many devices were tested for compression, shear, bruxism, and durability.
• Data Provenance: The bench testing was conducted internally by the manufacturer (Serena Sleep). The document doesn't specify the country of origin of this testing data. The data is retrospective in the sense that it's reported after the testing completion.

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

This section is not applicable as there was no clinical test set involving human subjects requiring expert ground truth establishment. The ground truth for device performance was established through engineering and mechanical testing methodologies against known maximum jaw forces from literature.

4. Adjudication Method for the Test Set

Not applicable, as no human-in-the-loop clinical test set was conducted that would require 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

Not applicable. This device is a physical intraoral appliance, not an AI-powered diagnostic tool, and no MRMC study was conducted.

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

Not applicable. This is not an algorithm-only device. The core "performance" was physical bench testing of the device for mechanical integrity and durability.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

For the bench testing, the "ground truth" for mechanical forces was established through:

• Literature search of maximum jaw strengths: This implies reliance on established scientific or engineering data regarding biomechanical forces in the human jaw.

For the comparison for substantial equivalence, the "ground truth" was the specifications and performance of the legally marketed predicate devices.

8. The Sample Size for the Training Set

Not applicable. This is a physical medical device, not an AI model that requires a training set.

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

Not applicable, as there was no training set.

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