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Vatech Clismile is indicated for orthodontic movement of natural teeth, excluding mandibular bicuspid teeth.

This product is an orthodontic bracket and is used with orthodontic wire materials and other orthodontic products to treat malocclusion of the upper and lower teeth.

It is used to correct teeth by transmitting physical force to each tooth, and is a selfligation bracket using a ceramic body and a metal cap made of a metal material. Shelf-life is not applicable because of the low likelihood of time-dependent product degradation.

The provided text describes a 510(k) premarket notification for a medical device called "Vatech Clismile," which is an orthodontic bracket. The document focuses on demonstrating substantial equivalence to a predicate device, "In-Ovation C." The information does not present a study with detailed acceptance criteria and performance reports in the context of an AI/ML powered device. Instead, it focuses on the equivalence of physical and materials characteristics.

Therefore, many of the requested fields cannot be filled as they pertain to the evaluation of an AI-powered medical device through clinical or performance studies with ground truth establishment, which is not the subject of this document.

However, I can extract the relevant information regarding the technical characteristics and the performance testing mentioned to demonstrate equivalence.

Here's the information based on the provided text, with many fields marked as "Not applicable" due to the nature of the document.

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

The document does not explicitly state "acceptance criteria" in a quantitative sense as might be expected for software or AI performance, but rather focuses on equivalence of physical/material properties and adherence to relevant standards.

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: Specific sample sizes are not explicitly stated for all tests. For "Dimensional analysis," specific models "Clismile 12-1223" and "IN-OVATION C H100-132-81" (corresponding to the maximum canine) were used for comparative assessment. The document states "Bracket generally uses a one-kit product consisting of 20 brackets." and that "Clismile is also composed of the same number of sets" as the predicate.
• Data Provenance: Not specified, but the manufacturing company is Rayence Co., Ltd. from Gyeonggi-do, Korea. The tests are likely internal company evaluations or performed by accredited labs.
• Retrospective/Prospective: Not specified, but given the context of a 510(k) submission, these would be controlled tests performed for regulatory submission.

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. This document does not describe a study involving expert readers or establishment of ground truth in a clinical assessment context. The evaluations are primarily engineering and materials performance tests.

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

Not applicable. (No expert adjudication involved for this type of technical performance testing).

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 is not an AI-powered device 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 AI algorithm.

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

The "ground truth" for the tests performed is based on:

• Engineering specifications and measurements (e.g., dimensions, torque, angulation).
• Material composition analysis.
• Standardized test methods (e.g., ISO 27020 for brackets, ISO 10993 series for biocompatibility, ISO 22674 for metallic materials).
• Direct comparison to the predicate device's established characteristics.

8. The sample size for the training set

Not applicable. There is no AI/ML model for which a training set would be required.

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

Not applicable. There is no AI/ML model.

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Clear On Mobile software carries out the image processing and administration of medical X-ray data which includes adjustment of window leveling, rotation, zoom, and measurements. Clear on Mobel is not approved for mammography and is meant to be used by qualified medical personnel only. Clear On Mobile is complying with DICOM standards to assure optimum communications between network systems.

ClearON Mobile provides easy touch based user interface to perform diagnosis of digital radiograph images displayed on ClearOn Mobile. ClearON Mobile receives digital images from a flat panel detector and a X-ray generator. The software also manages patient data, stores and transfers diagnostic images in an internal database.

It supports DICOM image file format which allows interoperability with other Radiography equipment in network environment.

The provided text is a 510(k) summary for the ClearON Mobile software. It describes the device, its functions, and a comparison to a predicate device. However, it does not contain information about an acceptance criteria table, reported device performance metrics, sample sizes (for test or training sets), data provenance, number or qualifications of experts establishing ground truth, adjudication methods, multi-reader multi-case studies, standalone performance, or the specific type of ground truth used.

The document only states that "The validation testing verified and validated the risk analysis and individual performance results were within the predetermined acceptance criteria." but does not elaborate on what those criteria were or what the performance results specifically were.

Therefore, I cannot fulfill the request to provide the detailed information about acceptance criteria and the study that proves the device meets them based on the input provided.

The document primarily focuses on demonstrating substantial equivalence to a predicate device by comparing features and stating that the pre and post image processing algorithms are identical. It does not present a performance study with detailed metrics against defined acceptance criteria.

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