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NobelClinician® (DTX Studio Implant) is a software interface for the transfer and visualization of 2D and 3D image information from equipment such as a CT scanner for the purposes of supporting the diagnostic process, treatment planning and follow-up in the dental and cranio-maxillofacial regions.

NobelClinician® (DTX Studio Implant) can be used to support guided implant surgery and to provide design input for and review of dental restorative solutions. The results can be exported to be manufactured.

NobelClinician® is a software interface used to support the image-based diagnostic process and treatment planning of dental, cranio-maxillofacial, and related treatments. The product will also be marketed as DTX Studio implant.

The software offers a visualization technique for (CB)CT images of the patient for the diagnostic and treatment planning process. In addition, 2D image data such as photographic images and X-ray images or surface scans of the intra-oral situation may be visualized to bring diagnostic image data together. Prosthetic information can be added and visualized to support prosthetic implant planning. The surgical plan, including the implant positions and the prosthetic information, can be exported for the design of dental restorations in NobelDesign® (DTX Studio design).

Surgical planning may be previewed using the software and the related surgical template may be ordered.

This document is a 510(k) premarket notification summary for the NobelClinician® (DTX Studio Implant) software. The information provided heavily focuses on regulatory comparisons to a predicate device rather than detailed study protocols and results for meeting specific acceptance criteria in a typical clinical performance study.

Based on the provided text, the device is a "Picture archiving and communications system" (PACS) software, classified under 21 CFR 892.2050. The performance data presented are primarily non-clinical.

Here's an attempt to answer your questions based only on the provided text:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present a table of acceptance criteria and reported device performance in the manner typically found in a clinical study report. Instead, it states that "Software verification and validation per EN IEC 62304:2006" was performed. This implies that the acceptance criteria would be related to the successful completion of these verification and validation activities, demonstrating that the software meets its specified requirements and is fit for its intended use, as defined by the standard.

Since specific performance metrics (e.g., accuracy, precision) for diagnostic or treatment planning tasks are not quantified or presented in this regulatory summary, a table showing such criteria and performance cannot be constructed from this document.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document does not provide details on the sample size used for a test set or the provenance of any data beyond indicating "non-clinical studies". This suggests that a traditional clinical test set with patient data for evaluating performance metrics was not the focus of the "performance data" section in this 510(k) summary. The listed activity "Software verification and validation per EN IEC 62304:2006" typically involves testing against synthetic data, simulated scenarios, or existing clinical datasets used for functional and performance testing of software, rather than a prospective clinical study with a defined "test set" in the sense of patient cases.

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 in the document. Given that the performance data mentioned refers to "Software verification and validation per EN IEC 62304:2006," it is unlikely that a formal ground truth establishment by a panel of clinical experts for a test set (as in a diagnostic accuracy study) was conducted or reported in this summary.

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

This information is not provided in the document.

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

The document does not mention any multi-reader multi-case (MRMC) comparative effectiveness study. The "performance data" section is limited to "Non-Clinical Studies - Software verification and validation per EN IEC 62304:2006".

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

The document does not explicitly state whether a standalone algorithm-only performance study was conducted. The device is described as "a software interface for the transfer and visualization of 2D and 3D image information from equipment... for the purposes of supporting the diagnostic process, treatment planning and follow-up...". This indicates a human-in-the-loop context for its intended use. The verification and validation activities would assess the software's functional correctness for these tasks.

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

Since the document only refers to "Software verification and validation per EN IEC 62304:2006" as performance data, the specific type of "ground truth" used for these non-clinical tests is not detailed. For software testing, ground truth could involve:

• Pre-defined expected outputs for given inputs.
• Comparisons to known good results from previous versions or manually calculated values.
• Adherence to specifications and design requirements.
  Formal clinical ground truth (like pathology or outcomes data) is not mentioned.

8. The sample size for the training set

This document does not indicate that the device involves AI/machine learning requiring a "training set" in the conventional sense. The "performance data" refers to software verification and validation, which focuses on the deterministic functionality of the software according to its specifications, not statistical learning from data.

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

As there is no mention of an AI/machine learning component requiring a training set, this information is not applicable and not provided in the document.

In summary, the provided FDA 510(k) summary focuses on demonstrating substantial equivalence to a predicate device based primarily on non-clinical software verification and validation activities. It does not contain detailed information about a clinical performance study with specific acceptance criteria, test sets, expert ground truth establishment, or comparative effectiveness studies of the type you've inquired about.

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