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Indications for CEREC Cercon 4D™ CAD/CAM Blocks are:

• Crowns and bridges in the posterior and anterior tooth region
• Bridges with a maximum of two pontics
• Inlays, onlays and veneers

CEREC Cercon 4D™ CAD/CAM Block are pre-sintered zirconia blocks composed of yttria stabilized zirconia, which are designed for CAD/CAM milling. The proposed CEREC Cercon 4D™ CAD/CAM Blocks possess a 3-dimensional contour of dentine powder to mimic the natural tooth build-up. The aim of the 3D contour technology is to achieve more aesthetic restorations than with blocks based on the current multi-layer technology. The CEREC Cercon 4D™ CAD/CAM Blocks are used for fabrication of a dental restoration. The CEREC Cercon 4D™ CAD/CAM Blocks are not provided as the finished, fully finished dental restoration. The blocks are single use, meaning that they can only be milled once and are not recommended for re-milling. The blocks are materials supplied to dental professionals that must be further processed/manufactured using CAD/CAM milling and subsequent sintering to fabricate all ceramic restorations and they are not intended to be reused as in the context of direct patient-applied devices and materials. Therefore, no reprocessing, cleaning or disinfection instructions apply because it is a dental material and not a finished dental restoration itself. After sintering of the restoration, the dental professional will finish the restoration via polishing or glazing, before insertion into the patient's mouth.

This document describes a 510(k) premarket notification for the "CEREC Cercon 4D CAD/CAM Blocks," a dental material. The information provided is for regulatory clearance and focuses on demonstrating substantial equivalence to existing predicate devices, rather than a clinical effectiveness study of an AI-powered diagnostic device.

Therefore, many of the requested fields related to AI performance, ground truth, expert adjudication, and comparative effectiveness studies are not applicable or cannot be extracted from this document.

Here's the information that can be extracted and a clear indication of what is not applicable or not provided in the document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally "Pass" for conformance to ISO standards and biocompatibility tests. The reported device performance is that it passed these tests.

2. Sample size used for the test set and the data provenance

This document describes non-clinical bench testing and biocompatibility assessments, not a study involving a "test set" of patient data for an AI algorithm. Therefore, this information is not applicable in the context of an AI device. The tests performed are on material samples, and the document does not specify the number of samples used for each test. The provenance of the test samples (e.g., country of origin) is not provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. This is for a dental material, not an AI diagnostic device requiring expert interpretation of a test set.

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

Not applicable. This document pertains to materials testing, not diagnostic performance with a test set requiring adjudication.

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 diagnostic device.

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

Not applicable. This is not an AI device.

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

The "ground truth" in this context is adherence to established international standards (ISO standards) for ceramic dental materials and biocompatibility assessment. The "truth" is whether the material meets the specified physical, mechanical, and biological performance requirements defined by these standards.

8. The sample size for the training set

Not applicable. This is not an AI device that requires a training set.

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

Not applicable. This is not an AI device that requires a training set.

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CEREC Cercon 4D™ Abutment System is intended for use in partially or fully edentulous mandibles and maxillae in support of single cement-retained restorations.

The system comprises three parts:

• CEREC Cercon 4D™ Abutment Block
• TiBase
• CAD/CAM system

The CEREC Cercon 4D™ ceramic structure cemented to the TiBase is recommended for two-piece hybrid abutments for single tooth restorations and hybrid abutment crowns, used in conjunction with endosseous dental implants.

The CEREC Cercon 4D Abutment Blocks, which are used for fabrication of a ceramic structure, two-piece hybrid abutments (meso-structure and crown) and abutment crowns, that are cemented to a TiBase (titanium base) used with dental implant systems. The CEREC Cercon 4D Abutment Blocks are not provided as the finished, fully assembled dental implant medical devices. The abutment blocks are materials supplied to dental professionals that must be further processed/manufactured using CAD/CAM technology and they are not intended to be reused as in the context of direct patient-applied devices and materials.

CEREC Cercon 4D™ Abutment Block are Yttria-doped zirconia blocks suitable for chairside and lab side use in fabrication of single cement-retained restorations. CEREC Ceron 4D™ Abutment Block are designed with a pre-drilled screw access channel and anti-rotation feature. The design allows for fabrication of a ceramic structure, two-piece hybrid abutments (mesostructure and crown) and abutment crowns, that are cemented to theBase (Titanium base) used with dental implant systems.

The provided document describes the substantial equivalence of the CEREC Cercon 4D™ Abutment Blocks and System, primarily focusing on non-clinical performance and material characteristics, rather than an AI/ML-based device. Therefore, many of the requested elements pertaining to AI/ML device studies (e.g., sample size for test set, data provenance, number of experts for ground truth, adjudication method, MRMC studies, standalone performance, training set details) are not applicable or cannot be extracted from this document.

However, I can extract information related to the acceptance criteria and study that proves the device meets those criteria from the perspective of a medical device (specifically, a dental abutment system), even without AI elements.

Here's the information based on the provided text, with Not Applicable (N/A) for fields that relate to AI/ML studies and are not covered in this document.

Acceptance Criteria and Device Performance for CEREC Cercon 4D™ Abutment Blocks, CEREC Cercon 4D™ Abutment System

The device under review is primarily a dental abutment system, and its performance is evaluated based on material properties, mechanical strength, and software integration, not on diagnostic accuracy or AI assistance.

1. Table of Acceptance Criteria and the Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set:
  • For Flexural Strength (Table 8.1): Not explicitly stated, but typically involves a certain number of samples to ensure statistical significance as per ISO 6872.
  • For Fatigue Testing (Table 8.2): "New fatigue testing was conducted on the worst-case combinations relating to the greatest angulation, the platform size and the gingival height for the proposed Dentsply Sirona TiBase/Dentsply Sirona Implant Systems and Third Party TiBase/Third Party Implant Systems (Camlog) combinations." The exact number of samples per test condition is not specified in the document, but standardized tests like ISO 14801 would stipulate a minimum.
  • For Sterilization Validation, Biocompatibility, and Software Validation: Not explicitly specified in terms of sample count in this summary.
• Data Provenance: The document does not specify the country of origin of the data. The tests are described as "non-clinical tests" and "performance bench testing," indicating laboratory-based studies. The document does not mention if the data is retrospective or prospective, as this distinction is more relevant for clinical studies.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. This device is not an AI/ML diagnostic tool requiring expert ground truth for image interpretation or similar. The "ground truth" (or more accurately, established performance standards) for this device is based on mechanical properties and ISO standards, which are objective and do not require expert human interpretation in the way an AI diagnostic system would.

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

Not applicable, as no human expert interpretation or consensus review is involved in the performance testing of this device (e.g., physical strength, material composition).

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-assisted diagnostic device; therefore, MRMC studies are irrelevant.

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

Not applicable. This is not an algorithm-based device. Its "system" aspect refers to the combination of the abutment block, TiBase, and CAD/CAM system for fabrication, not an AI algorithm. The performance described is of the physical components and the software's ability to constrain design parameters.

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

The "ground truth" for this device's performance is established by international consensus standards (e.g., ISO 6872, ISO 14801, ISO 10993, ISO 17665-1) for dental materials and implants, along with internal software integration requirements. These are objective, quantitative measures rather than subjective human interpretations or clinical outcomes data in the context of diagnostic accuracy.

8. The sample size for the training set

Not applicable. This device does not have a "training set" in the context of machine learning.

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

Not applicable. This device does not have a "training set" in the context of machine learning.

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