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Ottho Vision software is indicated for use by orthodontists for image analysis, simulation, profilogram, growth forecast, VTO/STO and patient consultation. Results produced by the software's diagnostic, treatment planning and simulation tools are dependent on the interpretation of trained and licensed practitioners or dentists.

OrthoVision is a 2D orthodontic analysis and simulation program created by EWOO Software. Ortho Vision manages patient information and images during orthodontic analysis. This software also assists in orthodontic treatment by providing diagnostic image analysis, growth forecasts, profilograms, superimpositions, and VTO/STO simulations. The analyzed results are saved in a chart format and the user can easily store and track treatment records of each patient.

The provided text for K131570 is a 510(k) summary for OrthoVision, an orthodontic analysis and simulation program. The document states that the device has been assessed and tested by the manufacturer and passed all in-house testing criteria. It also mentions a software validation test to evaluate input and output functions and states that individual performance results were within predetermined acceptance criteria. However, the document does not detail specific acceptance criteria, nor does it present the results of a study that objectively proves the device meets such criteria.

The submission focuses on establishing substantial equivalence to a predicate device (CS Orthodontic and OMS) by comparing intended use and technical characteristics, rather than providing a detailed performance study with acceptance criteria.

Therefore, much of the requested information cannot be extracted from this document, as a formal study with defined acceptance criteria and reported device performance, sample sizes, expert involvement, and ground truth establishment, as per the request, is not present.

Here's a breakdown of what can and cannot be provided based on the given text:

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

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

• Sample size for the test set: Not specified.
• Data provenance: Not specified.

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):

• Number of experts: Not specified.
• Qualifications of experts: Not specified.

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

• Adjudication method: Not specified.

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:

• MRMC study: No, a MRMC comparative effectiveness study is not mentioned. The device is a software tool for analysis and simulation, not necessarily designed to improve human reader performance in a diagnostic capacity in the way an AI algorithm might augment image interpretation. The document emphasizes that "Results produced by the software's diagnostic, treatment planning and simulation tools are dependent on the interpretation of trained and licensed practitioners or dentists."

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

• The document implies that standalone software validation was performed ("The software validation test was designed to evaluate all input functions, output functions, and actions performed by OrthoVision"). However, it does not provide details of its performance in a clinical context without human-in-the-loop, only that the software's results are dependent on human interpretation.

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

• Type of ground truth: Not specified. The document refers to "in-house testing criteria" and "predetermined acceptance criteria" but does not describe the nature of the ground truth used for validation.

8. The sample size for the training set:

• Sample size for training set: Not specified. (It's not clear if this device uses machine learning; the description makes it sound like a rule-based software program.)

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

• How ground truth was established: Not specified.

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