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Abutment Design is intended as an aid to the restoration in partially or fully edentulous mandibles and maxillae. Abutment Design is intended for use by a dental practitioner or dental laboratory staff for designing the patient specific component of a two-piece, or hybrid dental implant abutment. The resulting abutment design is intended to be used by the manufacturer of an endosseous dental implant abutment to create the final device.

The Abutment Design software receives scan data containing topographical characteristics of real teeth, dental impressions, or stone models. The software provides the user with the ability to create matching endosseous dental implant abutments using computer aided design.

The Abutment Design is restricted to be used with 510(k) cleared abutment systems, and the design output from Abutment Design (the patient specific part of the abutment) must be manufactured according to the instruction of the selected abutment system.

The Abutment Design software includes design parameters from the 510(k) cleared abutment systems such as implant type, maximum and minimum dimensions (e.g., abutment post height, gingival height, angulation, gingival margin diameter, etc.). The design parameters, provided by abutment system manufacturers, for an abutment system are available via a 3Shape server when documentation of the 510(k) clearance of said implant system is presented to 3Shape.

Abutment Design has no patient contact being a software only device.

I am sorry, but the provided text only discusses the comparability of "Abutment Design" software to a predicate device, and includes general statements about nonclinical testing for verification and validation without providing specific acceptance criteria or study details that would allow me to populate the requested table and answer the detailed questions about sample sizes, ground truth, expert qualifications, and specific study designs.

Therefore, I cannot fulfill your request for the specific information regarding acceptance criteria and study details.

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The 3Shape Abutment Designer Software is intended as an aid to the restoration of chewing function in partially or fully edentulous mandibles and maxillae. The 3Shape Abutment Designer is intended for use by a dental practitioner or dental laboratory staff for designing the patient specific component of a two-piece, or hybrid dental implant abutment. The single or multi-unit abutment design is intended to be used by the manufacturer of an endosseous dental implant abutment to create the final device.

The 3Shape Abutment Designer™ Software receives scan data containing topographical characteristics of real teeth, dental impressions, or stone models. The software provides the user with the ability to create matching endosseous dental implant abutments using computer aided design. The output of the device is a computer file containing the abutment(s) in digital form which can be used by manufacturers that hold an implant abutment 510(k) or are milling per the specific instructions provided by the holder of a 510(k) marketing clearance for a patient specific implant abutment. The 3Shape Abutment Designer Software output is restricted in the U.S. to be manufactured by a holder of an implant abutment 510(k) or milled per the specific instructions provided by the holder of a 510(k) marketing clearance for a patient specific implant abutment. The 3Shape Abutment Designer™ Software can be run on properly configured "off-the-shelf" PC hardware running Microsoft® Windows and using a standard keyboard and mouse. The 3Shape Abutment Designer™ Software requires the loading of implant libraries, which includes information such as implant type, maximum and minimum dimensional parameters for abutments, etc., created by separate abutment manufacturers and cleared by the FDA. In the US, the Implant Libraries are obtained via a 3Shape server after demonstration to 3Shape of the FDA clearance of the Implant Library.

The provided text describes the 3Shape Abutment Designer™ Software, its intended use, and the nonclinical testing performed for its 510(k) submission. However, it does not provide specific details on acceptance criteria or a detailed study proving the device meets those criteria, as typically found in a clinical study report.

The text focuses on the software's substantial equivalence to a predicate device (Sirona Dental CAD/CAM System) by comparing their intended use, technological characteristics, and software output. The "Nonclinical Testing" section broadly mentions "approved acceptance criteria" but without enumerating them or describing a specific study report that would contain the requested details.

Therefore, many of the requested elements cannot be extracted from the provided text.

Here's a breakdown of what can be inferred or extracted, and what is missing:

1. Table of acceptance criteria and the reported device performance

Missing: Specific, quantifiable acceptance criteria (e.g., accuracy metrics, specific error rates, precision targets) are not detailed. The actual performance outcome against numerical targets for these acceptance criteria is also not provided.

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

Missing: The text does not specify the sample size used for any test set or the data provenance (e.g., country of origin, retrospective/prospective nature of the data). "Test set" in this context refers to the data used for the "verification and validation testing" mentioned.

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

Missing: This information is not provided. The testing described is primarily software verification and validation, not a clinical study involving expert ground truth establishment for diagnostic or treatment efficacy.

4. Adjudication method for the test set

Missing: This information is not provided.

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

Missing: No MRMC comparative effectiveness study is mentioned. The device is software for designing abutments, not an AI for diagnosis or interpretation that would typically involve human readers.

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

The device is a standalone software component ("algorithm only" in the sense that it automates design based on input). The "Device Description" states it receives scan data and provides digital output. The "Nonclinical Testing" section confirms "Software, hardware, and integration verification and validation testing was performed." This implies standalone testing of the software's functionality.

However, specific performance metrics for this standalone functionality are not detailed.

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

Missing: The text does not explicitly state the type of "ground truth" used for its verification and validation. Given the nature of the software (CAD for dental abutments), the "ground truth" likely refers to established engineering specifications, design rules, and possibly simulated or physical models that conform to dental anatomical and functional requirements. It is not clinical "ground truth" in the diagnostic sense (like pathology or outcomes data).

8. The sample size for the training set

Missing: This is not applicable in the typical sense of a machine learning "training set." The 3Shape Abutment Designer™ Software appears to be a rule-based CAD software, not a machine learning algorithm that is "trained" on a dataset. It "requires the loading of implant libraries" which contain design information cleared by the FDA, but these are not "training sets."

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

Missing: As per point 8, a "training set" in the machine learning context is not applicable here. The "implant libraries" used by the software are based on information from actual implant manufacturers, cleared by the FDA, which function as pre-defined specifications and constraints rather than a statistical training dataset.

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