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The SmartByte device is intended to aid in weight management in overweight to obese individuals. The device is indicated for individuals with a body mass index (BMI) in the range of 27-35 kg/m² in conjunction with behavioral modification instruction.

The Scientific Intake SmartByte device is an oral removable palatal space occupying device that includes a temperature recording sensor to monitor usage. The device includes a Palatal Mold Kit used by a healthcare professional to make the SmartByte device and an optional SmartByte Reader component that connects to a mobile application wirelessly via Bluetooth to allow usage data to be uploaded to assess frequency of device usage, i.e., adherence and patient reported weight management.

The provided document is a 510(k) summary for the SmartByte Device, comparing it to a predicate device (Sensor Monitored Alimentary Restriction Therapy (SMART) Device). The core of the submission is to demonstrate substantial equivalence, primarily for a change in the software platform from a computer-based system to a wireless mobile application.

Based on the document, here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. A table of acceptance criteria and the reported device performance:

The document does not present explicit "acceptance criteria" in the typical sense of quantitative performance metrics for a medical device's efficacy (e.g., sensitivity, specificity, accuracy for a diagnostic device). Instead, the acceptance criteria are focused on demonstrating that the modifications to the predicate device (specifically, the mobile app and Bluetooth connectivity) do not raise new questions of safety or effectiveness and that the new components function as intended.

The "reported device performance" is a demonstration of equivalence and functional verification rather than clinical outcomes.

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 does not specify a "test set" in the context of clinical data for efficacy or a specific dataset for algorithm performance. The testing described is primarily software verification and validation testing and electrical safety testing (IEC 60950-1). These types of tests typically involve controlled environments, specific test cases, and simulation rather than a "sample size" of patient data or data provenance from a real-world setting.

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):

This information is not applicable and not provided for this 510(k) submission. The device is not an AI/ML diagnostic tool requiring expert-established ground truth for image interpretation or similar tasks. The "ground truth" for the software and hardware components would be established by engineering specifications, functional requirements, and recognized safety standards, which are verified through the V&V activities.

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

This information is not applicable and not provided. Adjudication methods are typically used in clinical studies or studies involving human readers/interpreters to establish a reference standard for ambiguous cases. As this submission focuses on software and hardware functional equivalence and safety, such methods are not relevant.

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 MRMC comparative effectiveness study was not done. This device is an oral appliance for weight management with a data logging/display component; it is not an AI-assisted diagnostic tool that would typically involve human readers interpreting cases.

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

The device's data upload and display function via the app could be considered an "algorithm only" component in terms of its internal logic. However, the document describes "software verification and validation" (page 5) which confirms its standalone functionality. There is no specific mention of a separate "standalone performance study" in the context of AI algorithm performance metrics (e.g., AUC, sensitivity, specificity). The focus is on the software correctly performing its designed function of data handling and display.

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

The "ground truth" in this context is based on engineering specifications, functional requirements, and recognized industry standards (e.g., ISO 10993 for biocompatibility, IEC 60950-1 for electrical safety). For the software, the ground truth is that the software correctly processes, stores, and displays data according to its design, and that the wireless communication functions reliably and safely. These are verified through various engineering and software testing methods, not through clinical "outcomes data" or "expert consensus" on diagnostic interpretations.

8. The sample size for the training set:

This information is not applicable and not provided. The device is not an AI/ML model that undergoes a "training" phase with a dataset. The software is developed based on programming logic and then verified through testing.

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

This information is not applicable and not provided for the same reason as point 8.

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