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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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CERANUM 87 is intended for manufacturing:

• -Inlay / Onlays
• -Crowns
• Bridges -
• -Implant superstructures

CERANUM 87 is a High Noble, Micro-fine, Yellow, Gold based alloy to be used for inlays, onlays, single crowns, bridge, implant superstructures and substrate for medium expansion with lower fusing porcelains and indirect restorative composites. CERANUM 87 is a high gold ceramic alloy, which heightens the porcelain esthetics of the restoration and provides strength, durability and color of gold.

The provided document, KO21947, is a 510(k) premarket notification for a dental casting alloy named CERANUM 87. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than conducting extensive clinical studies to evaluate device performance against specific acceptance criteria in the same way a diagnostic AI device would.

Therefore, many of the requested elements for a clinical study on AI device performance are not applicable to this document. The document describes a comparison of physical and chemical properties of a dental alloy, not a clinical study involving human patients or complex performance metrics for a diagnostic device.

Here's a breakdown based on the provided document and the nature of a 510(k) for a dental alloy:

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

   There isn't a direct "acceptance criteria" table for a diagnostic AI device's performance in the clinical sense. Instead, the "acceptance criteria" can be inferred from the comparison of the CERANUM 87 alloy's properties to those of the predicate device, DEGUDENT GS. The implicit acceptance criterion is that the new device's properties are "substantially equivalent" to the predicate.

*Implicit Acceptance Criterion*: The values for CERANUM 87 are considered "almost identical" or within a range that does not affect safety or effectiveness, leading to a determination of substantial equivalence. The document states: "The main elements and their concentration are almost identical." and "Despite minor differences in the materials, we believe that CERAUM 87 is a substantially equivalent to DEGUSSA AG's DEGUDENT GS. These changes 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)

*   **N/A.** This is a comparison of alloy properties based on standard test methods (ANSI/ADA 5 and ISO 9693), not a clinical study with a patient test set. The "samples" would refer to tested batches of the alloy, not a patient population. Data provenance (country of origin, retrospective/prospective) is not mentioned as it's not relevant for this type of 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)

*   **N/A.** Ground truth in the context of diagnostic performance is not applicable here. The "ground truth" for material properties is established by the results of the standardized physical and mechanical tests themselves.

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

*   **N/A.** No adjudication method is described or required for this type of material property comparison.

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

*   **N/A.** This is not a diagnostic AI device, so no MRMC study or assessment of human reader improvement is relevant.

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

*   **N/A.** Not an AI algorithm.

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

*   **Material Property Standards/Test Results.** The "ground truth" related to the device's characteristics are the quantitative measurements of its physical and mechanical properties, as determined by applying recognized industry standards like ANSI/ADA 5 and ISO 9693.

8. The sample size for the training set

*   **N/A.** This is not an AI/machine learning device, so there is no training set.

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

*   **N/A.** Not an AI/machine learning device.

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