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The DENTRIa series is a Computed Tomography X-Ray imaging device specialized in diagnosing general dental treatments and orthodontic purpose using Panoramic and Cephalometric images respectively. In addition DENTRIC series is used in the field of Otolaryngology by capturing 360 degree rotation sequence of the head and neck areas, including the ENT and dentomaxillofacial areas for a dental treatment in adult and pediatric dentistry and obtains x-ray images from different angles and calculate through computer-processed to produce 3D x-ray tomographic images. The DENTRIC series used by physicians, dentists, and x-ray technologists.

This Equipment is a Dental X-Ray imaging device used for diagnostic purpose in dental treatment. The operating principle of this device is obtaining the tomographic, and panoramic images by rotating arm to get the recombination data. X-ray generator and detector rotate around the patient to irradiate the X-ray, and penetrated X-ray is measured by the detector. When the X-ray is irradiated on the teeth area for instance, large amount of X-ray is attenuated because objects such as bones are highly dense. On the contrast, X-ray is more permeable to small molecules with low density such as skin or tissue, so more X-ray would pass through the subject. By measuring data obtained from measuring the X-ray is reconstructed by the software to display and analyze the anatomical structure for the diagnosis purposes.

The provided text is a 510(k) summary for the DENTRIa series of computed tomography X-ray systems, which focuses on demonstrating substantial equivalence to a predicate device. It contains information about non-clinical testing for imaging performance but does not describe a study that uses acceptance criteria for algorithm performance (e.g., sensitivity, specificity, or accuracy) against a defined ground truth for a test set, which would typically be present for an AI/ML medical device.

Therefore, I cannot provide a table of acceptance criteria and reported device performance for an AI/ML algorithm or details about a study evaluating its performance in the requested format.

However, I can extract information related to the device's technical performance testing that is present in the document.

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

As stated above, this document primarily discusses the substantial equivalence of an X-ray imaging device to a predicate device based on technical characteristics and general performance, not the performance of an AI/ML algorithm against clinical acceptance criteria. The document lists technical specifications rather than specific clinical performance criteria for an AI/ML component.

Below is a table summarizing some of the technical performance evaluations mentioned:

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 describe a test set or data provenance in the context of validating an AI/ML algorithm's clinical performance. The "performance test" section mentions "bench testing" and "image performance testing" conducted according to IEC 61223-3-4 and SSXI (Solid State X-ray Imaging) Devices guidance. These are hardware and imaging physics performance tests, not clinical evaluations with patient data.

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. The document does not describe a clinical ground truth establishment process for an AI/ML component.

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

Not applicable.

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. The submission is for an X-ray imaging device, not an AI/ML software assistance tool for readers.

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

Not applicable. The document describes an X-ray imaging system, which inherently involves human operation and interpretation. The "software validation" section mentions "original software and OTS software as an image viewer" and that the "algorithm type of image reconstruction is FBP (Filtered Back Projection)," but this is about image reconstruction, not a standalone diagnostic AI algorithm.

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

Not applicable. For the technical performance tests mentioned (MTF, DQE), the "ground truth" would be established physical standards and measurement techniques.

8. The sample size for the training set:

Not applicable. There is no mention of a training set for an AI/ML algorithm.

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

Not applicable.

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