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LUCIUS 62 is intended for manufacturing:

• Inlay / Onlays -
• Crowns -
• Short span bridges -
• Long span bridges -
• Removable partials -

LUCIUS 62 is a platinum-free crown and bridge alloy. This device is dependable 62% gold alloy with a high gold appearance. LUCIUS 62 is excellent for inlays, three-quarter crowns, long and short-span bridges.

The provided text describes the 510(k) summary for a dental casting alloy, LUCIUS 62. This summary is not about a device that uses AI or involves image analysis, but rather a metal alloy for dental restorations. Therefore, many of the requested points regarding AI/ML device studies (such as sample size for test set, data provenance, number of experts, adjudication method, MRMC studies, standalone performance, training set details, and ground truth establishment methods typical for AI/ML) are not applicable to this document.

The document focuses on establishing substantial equivalence to a predicate device (Jel-62) based on chemical composition and physical/mechanical properties, as per traditional medical device regulations for materials.

Here's an attempt to answer the applicable questions based on the provided text:

Acceptance Criteria and Device Performance

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

The acceptance criteria are implied by the comparison to the predicate device, Jel-62, and adherence to "Test methods applied: as in ANSI/ADA 5 and ISO 9693." The device performance is considered acceptable if it is "substantially equivalent" to Jel-62. No explicit target ranges or pass/fail thresholds are given, but rather a direct comparison to the predicate.

Discussion on Acceptance: The document states, "Since the composition of the legally marketed alloy and the new device is very similar, it may be assumed that the biological compatibility of the alloys is also very similar." And concludes: "LUCIUS 62 is substantially equivalent to JELENKO'S Jel-62, and the minor differences between them do not affect safety or effectiveness."

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

This information is not provided in the summary. The document does not specify sample sizes for mechanical testing or details on data provenance (e.g., country of origin, retrospective/prospective nature). It simply presents comparison 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)

This is not applicable. The "ground truth" here is the established properties of the predicate device and the new device as measured by standard material testing methods (ANSI/ADA 5 and ISO 9693), not expert interpretation of complex data (like medical images).

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

This is not applicable. Adjudication methods are typically relevant for ambiguous cases in diagnostic studies resolved by expert consensus, which is not the nature of this submission.

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

This is not applicable. This device is a dental casting alloy, not an AI-powered diagnostic tool, so no MRMC study or AI assistance evaluation would be relevant.

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

This is not applicable. This is not an algorithm or an AI device.

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

The "ground truth" for the comparison is the physical and mechanical properties of the materials, measured according to recognized industry standards (ANSI/ADA 5 and ISO 9693). It is objective measurement data, not expert consensus, pathology, or outcomes data in the usual sense.

8. The sample size for the training set

This is not applicable. This is not an AI/ML device, so there is no training set in that context.

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

This is not applicable. As there is no training set mentioned or implied for an AI/ML context, the method for establishing its "ground truth" is irrelevant.

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