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CADImplant is intended for pre-treatment software planning for the placement of dental implants using a CT scan which has been input into the CADImplant treatment planning software.

The CADImplant software is specifically designed for use in dental implant procedures. It allows the dentist to locate dental implants on three planes (axial, sagittal and frontal) on a pre-treatment CT scan in real-time. Additionally, the software allows for the patient's prosthetic template to be pre-drilled according to the planning.

The CADImplant 510(k) summary provides limited information regarding specific acceptance criteria and detailed study results. Based on the provided text, here's an analysis:

Acceptance Criteria and Reported Device Performance

Study Information:

1. Sample Size used for the test set and the data provenance:

   • Test Set Sample Size: Not explicitly stated in the provided text. The document only mentions "summaries of accuracy testing using phantoms and clinical experience." This implies that phantoms and likely clinical cases were used as test sets, but the number of each is not specified.
   • Data Provenance: Not explicitly stated. The mention of "clinical experience" suggests human patient data was used, but details like country of origin or whether it was retrospective or prospective are not provided.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not explicitly stated in the provided text.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not explicitly stated.

4. 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 explicitly stated. The primary focus of this submission is on the CADImplant software itself rather than its comparative effectiveness with or without human readers. The document states its intended use is for "pre-treatment planning for the placement of dental implants," implying assistance to a clinician, but no MRMC study details are provided.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The document mentions "accuracy testing using phantoms." It is highly likely that these phantom tests were conducted in a standalone manner to assess the algorithm's performance in measuring implant placement parameters without human intervention. However, the outcomes of this testing are not detailed.

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

   • For phantom testing: The ground truth would typically be the known, precisely engineered dimensions and positions within the phantom.
   • For clinical experience: The ground truth is not specified, but for pre-treatment planning, it would ideally involve post-operative assessment (e.g., actual implant position relative to planned position, anatomical landmarks, patient outcomes regarding implant stability and function). The document only mentions "clinical experience," which is broad.

7. The sample size for the training set: Not explicitly stated.

8. How the ground truth for the training set was established: Not explicitly stated. If machine learning was involved (which is common for "software systems"), ground truth for training data would have been established through expert annotations or referencing known anatomical landmarks from CT scans. However, the document does not elaborate on the training process.

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