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• Aiding diagnosis in the oral-maxillofacial region
• Aiding comparisons of different treatment options
• · Aiding endodontic treatment planning
• · Aiding treatment planning for endodontic surgical guides

SICAT Endo is a pure software device. SICAT Endo is a software tool intended for viewing and analyzing medical information:

• medical 3D volume data such as volumetric X-ray data from Cone Beam CT (CBCT) and CT scanners, and
• . intraoral images, and
• . 3D optical surface data like optical impression data from optical scanners, and
  SICAT Endo provides tools for analyzing the root canal and to mark it visually. It allows to define a drill canal and ordering of a corresponding surgical quide with a drill sleeve.

The provided document is a 510(k) summary for the SICAT Endo device. It outlines the device's intended use, comparison to predicate devices, and non-clinical performance testing. The information required for a detailed description of acceptance criteria and the study proving compliance is scattered throughout the text.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document explicitly states quantitative acceptance criteria for length and angular measurements.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify the sample size used for the test set.
It states that "Special bench testing has been performed with non-clinical data" to verify the endodontic planning visualization quality and effectiveness and overall quantitative accuracy of the root canal treatment planning. The provenance of this "non-clinical data" (e.g., country of origin, retrospective/prospective) is not explicitly mentioned. Given the device is for dental applications and comparison to a predicate sold in the EU/USA, the data would likely be from relevant patient populations, but this is not stated.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

The document does not provide information on the number of experts used or their qualifications for establishing ground truth during the non-clinical bench testing.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method used for the test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or the effect size of human readers improving with AI vs. without AI assistance. The testing focuses on "non-clinical data" and "visualization quality and effectiveness," and "quantitative accuracy," implying an evaluation of the software's inherent functions rather than human-in-the-loop performance.

6. Standalone Performance Study

Yes, a standalone study (algorithm-only performance) was done. The "Special bench testing" focused on verifying the "endodontic planning visualization quality and effectiveness" and "overall quantitative accuracy of the root canal treatment planning." This type of testing evaluates the device's performance in isolation.

7. Type of Ground Truth Used

The document states "Special bench testing has been performed with non-clinical data" to verify accuracy. For quantitative accuracy measurements like length and angle, the ground truth would typically be established based on known physical measurements or highly precise anatomical references within the non-clinical data (e.g., phantoms, synthetic models, or highly accurate previous measurements from a gold standard). The specific nature (e.g., expert consensus, pathology, outcomes data) is not explicitly stated beyond "non-clinical data." However, for "quantitative accuracy," it can be inferred that a precisely measured ground truth would be used.

8. Sample Size for the Training Set

The document does not provide any information regarding the sample size for the training set. This is a 510(k) summary for a premarket notification, and such details about internal model development (like training data) are not typically required or disclosed at this stage for a device like this, which is described more as a "visualization" and "planning" software rather than a purely AI-driven diagnostic tool that learns from large datasets. The device description emphasizes "tools for analyzing the root canal and to mark it visually" and "define a drill canal," suggesting a feature-based software rather than deep learning that requires extensive training data.

9. How the Ground Truth for the Training Set Was Established

Since no information on a training set is provided, the method for establishing its ground truth is also not available in this document.

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