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SICAT Implant is a software application of imaging information of the oral-maxillofacial region. The imaging data originates from medical scanners such as CT or CBCT scanners. SICAT Implant is intended for use as planning software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments. The dental professionals' planning data can be exported from SICAT Implanning data includes in particular the positions and types of implants and drill sleeves to be used in the surgical procedures. This data may be used as input to design and manufacture surgical guides for dental implants.

SICAT Implant V2.0 is a pure software device. SICAT Implant V2.0 is a software application for the visualization of imaging information of the oral-maxillofacial region. The imaging data originates from medical scanners such as CT or CBCT scanners. SICAT Implant V2.0 is intended for use as planning software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments. SICAT Implant V2.0 allows to name, position, move, rotate, resize and visualize dental implants and other planning objects (i.e. nerves) within the visualized 3D volume. Thus, dental professionals like implantologists are enabled to precisely plan the positions, orientations, types and sizes of implants to be placed in the patient's mandible/maxilla together with the related surgical procedures. The dental professionals' planning data can be exported from SICAT Implant. This planning data includes in particular the positions, orientations and types of implants and drill sleeves to be used in the surgical procedures. This data may be used as input to design and manufacture surgical quides for dental implants.

The provided text, a 510(k) summary for SICAT Implant V2.0, primarily focuses on demonstrating substantial equivalence to a predicate device (SICAT Implant V1.2) rather than presenting a detailed performance study with explicit acceptance criteria. Therefore, several requested pieces of information are not available in this document.

However, based on the information provided, we can deduce some aspects of the device's validation and the general nature of its acceptance criteria.

1. Table of Acceptance Criteria and Reported Device Performance:

   The document does not provide a quantitative table of acceptance criteria and reported device performance in the format typically seen for novel AI algorithms demonstrating clinical effectiveness. Instead, it relies on demonstrating consistency with the predicate device and success in general software verification and validation activities.

   Inferred Acceptance Criteria (Qualitative) and Reported Performance:

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

   The document does not specify a "test set" in the context of a clinical performance study with a particular sample size of patient data. The testing described is primarily in the realm of software verification and validation (unit tests, integration tests, system verification and validation tests, usability tests). Data provenance (country of origin, retrospective/prospective) related to patient data is not mentioned because this is a software update (V2.0 of SICAT Implant) and its substantial equivalence is being demonstrated against a previous version (V1.2).

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 information is not provided. Since the device is a planning software, and not an AI diagnostic algorithm that produces a "ground truth" for disease detection, the concept of establishing ground truth by expert consensus would not apply in the same way. The software's "accuracy" is defined by its ability to correctly measure and manipulate 3D data as per its specifications (e.g., bone density, nerve canals, implant positions). The ground truth for measurement accuracy would typically be established through phantom or calibrated physical models.

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

   Not applicable in the context of this 510(k) submission, as it focuses on software verification and equivalence rather than a reader study adjudicating findings.

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:

   No MRMC study is mentioned. This device is described as "planning software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments." It's not presented as a device with AI assistance that's being compared to unassisted human readers for diagnostic improvement.

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

   The document states "SICAT Implant V2.0 is a pure software device." It's a planning tool for dental professionals, not an AI algorithm that makes a standalone decision or diagnosis. Its performance is always in the context of aiding a human professional. The stated measurement accuracies (100 µm, 1 degree) could be considered "standalone" technical performance metrics for the software's internal calculations.

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

   For the core functionalities like measurement accuracy, the ground truth would typically be established through validation against known physical phantoms or highly precise imaging standards, as indicated by the stated accuracies of 100 µm and 1 degree. For general software function, the "ground truth" is that the software performs according to its functional specifications, as verified by various software tests.

8. The sample size for the training set:

   This information is not applicable and not provided. The document describes a traditional software development process ("complete reengineering including significant software re-write") rather than a machine learning model that requires a training set.

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

   Not applicable, as no machine learning training set is described.

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