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The NobelClinician software is a software interface for the transfer and visualization of imaging information from equipment such as a CT scanner or a magnetic resonance scanner for the purposes of diagnosis and treatment planning in the dental and cranio-maxillofacial regions. The NobelClinician software can be used to design a surgical template for the purposes of aiding placement of dental implants.

NobelClinician is a software interface for the transfer and visualization of imaging information: 3D imaging like medical or Cone Beam CT data, 2D imaging like photographic images and X-ray images. NobelClinician is used to support diagnostics and treatment planning for dental and cranio-maxillofacial treatment through the use of prosthetic-driven implant planning based on the digitized patient data and the scanned radiographic guide representing the ideal diagnostic tooth setup. The planning can be previewed using the software and a surgical template realizing the planning can be ordered.

The provided 510(k) summary for NobelClinician (K123976) does not contain detailed information regarding specific acceptance criteria for a device performance study or the results of such a study in a quantitative manner (e.g., sensitivity, specificity, accuracy).

Instead, the submission primarily focuses on demonstrating substantial equivalence to predicate devices through a qualitative comparison of features, intended use, and indications for use.

Here's an analysis based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not include a table of acceptance criteria or quantitative device performance metrics (e.g., sensitivity, specificity, accuracy, dice score, etc.). The "Summary of testing to demonstrate safety and effectiveness" section states: "The performance of the NobelClinician software was verified and validated following the guidance provided in Guidance for the Content of Premarket Submissions for Software in Medical Devices." This indicates that verification and validation activities were performed, but the specific acceptance criteria and the results demonstrating meeting those criteria are not detailed in this 510(k) summary. The submission focuses on qualitative comparisons to predicates.

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

The document does not specify a sample size for a test set or provide details on data provenance (e.g., country of origin, retrospective or prospective data). The V&V activities likely involved internal testing rather than a clinical study with a defined test set of patient data, at least not in a way that would be summarized with these details in this type of 510(k) submission.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not mention the number of experts used to establish ground truth or their qualifications. Given the nature of the submission (substantial equivalence through feature comparison), a formal ground truth establishment process by external experts for a novel performance claim is not indicated.

4. Adjudication Method for the Test Set

No details on an adjudication method are provided, as a formal test set with ground truth established by experts is not described in this summary.

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

An MRMC comparative effectiveness study is not mentioned or described. The submission does not claim to improve human reader performance with AI assistance. The device is software for visualization and planning, not an AI-powered diagnostic aid that would typically warrant such a study.

6. Standalone Performance Study

A standalone performance study (algorithm only performance without human-in-the-loop) is not explicitly detailed with quantitative metrics. The V&V activities likely included internal functional and performance testing, but the results are not presented in a traditional standalone performance study format in this summary.

7. Type of Ground Truth Used

The type of ground truth used is not explicitly stated. For a dental/cranio-maxillofacial planning software, ground truth for verification and validation would likely involve:

• Engineering specifications/measurements: Comparing software output (e.g., measurements, implant placement coordinates) against known inputs or manually verified measurements.
• Expert consensus (implicit): Clinical validation of planning by dental professionals, though not described as a formal ground truth establishment process for a performance study in this summary.
• Predicate device comparison: Functional and performance equivalence to the predicate devices.

8. Sample Size for the Training Set

The document does not mention a training set sample size. This device is described as a "software interface for the transfer and visualization of imaging information" and a "planning software." It's not explicitly presented as an AI/machine learning device that would require a distinct training set in the modern sense. The "training" in this context would more likely refer to the iterative development and internal testing of the software.

9. How Ground Truth for the Training Set Was Established

As no training set is discussed in the context of machine learning, no information on how its "ground truth" was established is provided.

In summary, the 510(k) K123976 primarily relies on demonstrating substantial equivalence through a feature-by-feature comparison with legally marketed predicate devices, rather than presenting a detailed device performance study with specific acceptance criteria and quantitative results from a clinical test set. The statement about "verified and validated following the guidance" indicates that internal testing was done, but the specific metrics and methodology are not part of this public summary.

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