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DETECTAR is indicated for use in the detection of subgingival dental calculus.
DETECTAR is indicated for the detection of subgingival dental calculus

The DETECTAR probe is similar in intended use, size, and shape to a manual periodontal probe. The DETECTAR probe contains an optical fiber that reads the optical signature of dental calculus and converts it into an electrical signal. From that electrical signal a computer analysis identifies the dental calculus.

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated in the provided document. However, the study aims to show that DETECTAR performs better than a manual periodontal probe in detecting subgingival dental calculus. The reported device performance is that "The DETECTAR significantly outperformed the manual periodontal probe" in an in vitro evaluation.

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

• Sample Size: Not explicitly stated. The study involved a "piece of pig gingiva... on the root surface of the teeth" and a comparison with a manual periodontal probe. The number of teeth or calculus samples tested is not quantified.
• Data Provenance: In vitro evaluation. The country of origin is not specified but the submitter is from Quebec, Canada. Retrospective or prospective is not applicable for an in vitro study of this nature.

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

• Number of Experts: Three experienced clinicians.
• Qualifications: Described as "experienced clinicians." Specific qualifications (e.g., years of experience, specialty) are not provided beyond "experienced." The document implies these clinicians are performing the evaluations and their observations contribute to the findings.

4. Adjudication Method for the Test Set

The document does not describe an explicit adjudication method. The three experienced clinicians appear to have individually conducted the evaluations and their findings were then compared, leading to the conclusion that DETECTAR "significantly outperformed" the manual probe. It does not mention a consensus-building or tie-breaking process.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described in terms of human readers improving with AI vs. without AI assistance. The study described is a comparison of a device (DETECTAR) against a manual instrument (periodontal probe) in vitro, with clinicians performing the evaluations. It's not a study of human readers' diagnostic accuracy before and after AI assistance.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was effectively done. The DETECTAR device, which contains an optical fiber and uses "computer analysis" to identify dental calculus, was evaluated on its own in detecting calculus, with the output then presumably interpreted by the clinicians. The "significant outperformance" refers to the device's capability relative to a manual probe.

7. Type of Ground Truth Used

The ground truth used is implicitly the known presence or absence of subgingival dental calculus on the in vitro model (pig gingiva and tooth root). The "drops of blood" were introduced to simulate conditions, suggesting a controlled environment where the presence of calculus could be pre-established or observed reliably by the "experienced clinicians." It is not explicitly stated if a gold standard like histology or micro-CT was used to definitively label the calculus, but rather the clinicians' assessments appear to contribute to the understanding of ground truth or at least the comparative performance.

8. Sample Size for the Training Set

The document does not provide any information about a training set since this appears to be a direct evaluation of the device's performance rather than a validation of a machine learning model that would require a separate training phase. The "computer analysis" identifies dental calculus from an electrical signal, implying a pre-trained algorithm, but the details of that training are not included.

9. How Ground Truth for the Training Set Was Established

Not applicable, as information regarding a training set is not provided in the document.

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