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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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The SICAT Implant V 1.2 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 DVT scanners. The SICAT Implant V 1.2 is intended for use as planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments. The dental professionals' planning data may be exported from SICAT Implant V 1.2 and used as input data for CAD or Rapid Prototyping Systems.

SICAT Implant V1.2 is a pure software device. SICAT Implant V1.2 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 DVT scanners. SICAT Implant V1.2 is intended for use as planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments.

SICAT Implant V1.2 allows to name, position, move, rotate, resize and visualize dental implants and other planning objects (i.e. nerve canals) 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 may be exported from SICAT Implant V1.2 and used as input data for CAD or Rapid Prototyping Systems.

The provided text describes the SICAT Implant V 1.2 device and its 510(k) submission, but it does not contain information about specific acceptance criteria or a detailed study proving the device meets those criteria, as typically found in a clinical study report.

The text focuses on establishing substantial equivalence to predicate devices based on intended use, features, and technical characteristics. It mentions "Performance testing to validate the safety and effectiveness of the SICAT Implant V 1.2 system included validation testing and bench tests of the software functions," but provides no details on these tests, their results, or the methodology.

Therefore, I cannot fulfill your request for a table of acceptance criteria, reported device performance, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth details, or training set information. This information is not present in the provided 510(k) summary.

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SICAT Implant 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 DVT scanners. SICAT Implant is intended for use as planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments. The dental professionals' planning data may be exported from SICAT Implant and used as input data for CAD or Rapid Prototyping Systems.

SICAT Implant is a pure software device. SICAT Implant 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 DVT scanners. SICAT Implant is intended for use as planning and simulation software to aid qualified dental professionals in the placement of dental implants and the planning of surgical treatments. SICAT Implant allows to name, position, move, rotate, resize and visualize dental implants and other planning objects (i.e. nerve canals) 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 may be exported from SICAT Implant and used as input data for CAD or Rapid Prototyping Systems.

The provided text is a 510(k) summary for the SICAT Implant, a software device. It states that the device is "substantially equivalent" to predicate devices (SimPlant System and GALILEOS Implant System) based on intended use, features, and technical characteristics. The summary mentions "Performance testing to validate the safety and effectiveness of the SICAT Implant system included validation testing and bench tests of the software functions." However, it does not provide details regarding specific acceptance criteria, study methodologies, sample sizes, ground truth establishment, expert involvement, or statistical results for performance.

Therefore, the requested details about acceptance criteria and the study proving the device meets them cannot be extracted from this document. The document primarily focuses on establishing substantial equivalence to previously cleared devices rather than presenting a detailed performance study against specific acceptance criteria.

Based on the provided text, the following information is missing:

• A table of acceptance criteria and the reported device performance: Not provided.
• Sample sized used for the test set and the data provenance: Not provided.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided.
• Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not provided.
• 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 indicated or described.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not explicitly stated or detailed. The device is described as "aid[ing] qualified dental professionals," implying a human-in-the-loop scenario, but no performance metrics are given.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not provided.
• The sample size for the training set: Not provided.
• How the ground truth for the training set was established: Not provided.

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