(27 days)
belleGlass HP DC Opaceous Dentin LCTE 2 is a dual cured composite material for use with the belleGlass HP (heat and pressure) crown and bridge fabrication system where a more durable base for composite crowns and bridges is desired.
belleGlass HP DC Opaceous Dentin LCTE 2 is dual cured composite material for use with the belleGlass HP (heat and pressure) crown and bridge fabrication system where a more durable base for composite crowns and bridges is desired. The belleGlass HP crown and bridge fabrication system is comprised of all the components necessary for a dental laboratory to fabricate composite resin-based crowns and bridges and cure them using both light activation combined with a final heat and pressure curing cycle in the belleGlass HP automatic curing device. belleGlass HP was formulated to have a lower coefficient of thermal expansion to more closely match that of natural tooth dentin.
The provided text is a 510(k) summary for a dental material (belleGlass HP DC Opaceous Dentin LCTE 2) and related FDA correspondence. It does not contain information about acceptance criteria, device performance metrics, or study designs typically associated with AI/ML-driven medical devices.
The document states that the device is "substantially equivalent" to a legally marketed predicate device, belleGlass HP DC Opaceous Dentin LCTE, based on having similar function and intended use. This means its safety and effectiveness are established by comparison to an already approved device, rather than through a new clinical performance study with specific acceptance criteria and detailed quantitative results as would be typically found for novel devices or AI/ML algorithms.
Therefore, I cannot provide the requested information about acceptance criteria and study details for areas like sample size, ground truth, expert qualifications, or MRMC studies because this type of information is not present in the provided text for this specific device.
If the prompt were for an AI/ML medical device, the following would be the expected information:
1. A table of acceptance criteria and the reported device performance:
Not applicable to this document. This would typically include specific metrics like sensitivity, specificity, accuracy, AUC, F1-score, or precision/recall with predefined thresholds the device needs to meet.
2. Sample size used for the test set and the data provenance:
Not applicable to this document. For an AI/ML device, this would detail the number of cases/patients in the test set, their demographic breakdown, and where the data originated (e.g., specific hospitals, countries, retrospective or prospective collection).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable to this document. For an AI/ML device, this would specify how many experts reviewed the test data to define the true labels, along with their specializations, years of experience, and certifications.
4. Adjudication method for the test set:
Not applicable to this document. For an AI/ML device, this would describe the process if multiple experts disagreed on the ground truth (e.g., 2+1 means two experts decide, and a third adjudicates disagreements; 3+1 means three experts decide, and one adjudicates disagreements).
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 to this document. This type of study demonstrates the clinical utility of an AI device by comparing human performance with and without AI assistance, quantifying the improvement in metrics like diagnostic accuracy or reading time.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not applicable to this document. This refers to studies evaluating the AI algorithm's performance in isolation, without human intervention.
7. The type of ground truth used:
Not applicable to this document. For an AI/ML device, this would specify if ground truth was established by expert consensus, histology/pathology, long-term patient outcomes, or other definitive diagnostic methods.
8. The sample size for the training set:
Not applicable to this document. For an AI/ML device, this would be the number of cases/patients used to train the algorithm.
9. How the ground truth for the training set was established:
Not applicable to this document. For an AI/ML device, this would detail the method by which the true labels for the training data were determined, similar to the test set ground truth but often with less stringent adjudication for initial training.
§ 872.3690 Tooth shade resin material.
(a)
Identification. Tooth shade resin material is a device composed of materials such as bisphenol-A glycidyl methacrylate (Bis-GMA) intended to restore carious lesions or structural defects in teeth.(b)
Classification. Class II.