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Aid in diagnosis of dental pits and fissures and interproximal caries and aid in the detection of dental calculus.

The D-Carie probe contains optical fibers that read the optical scattering characteristic of dental carie thru healthy enamel structure above (the marginal ridge over the vulnerable inteproximal area) and convert it into an electrical signal. From that signal a computer analysis identifies the dental caries. The device can be in either in a new handheld or a tabletop (original) format with the addition or not of the calculus detection feature of the Detectar.

Here's a breakdown of the acceptance criteria and study information for the Neks Technologies, Inc. D-Carie/Detectar device based on the provided text:

Acceptance Criteria and Device Performance

The FDA 510(k) summary does not explicitly state numerical acceptance criteria in a table format. Instead, it indicates that the device's performance for interproximal caries detection is "at least equivalent in efficiency to the FOTI/DIFOTI devices or visual examination."

Study Details

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

   • Test Set Sample Size: Not explicitly stated. The document mentions "in vitro and in vivo studies" but does not quantify the number of cases or subjects.
   • Data Provenance: Not specified, but given the company is based in Quebec, Canada, it's plausible data was collected in Canada, though this is not confirmed. The document doesn't mention if it was retrospective or prospective.

2. Number of experts used to establish the ground truth for the test set and qualifications of those experts:

   • Number of Experts: Not specified.
   • Qualifications: Not specified.

3. Adjudication method for the test set:

   • Not mentioned.

4. If a multi-reader, multi-case (MRMC) comparative effectiveness study was done:

   • No MRMC comparative effectiveness study is described where human readers improve with AI vs. without AI assistance. The comparison is between the device and existing methods (FOTI/DIFOTI, visual examination, bitewings x-rays) as a standalone diagnostic aid.

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

   • Yes, the studies described assess the D-Carie/Detectar device as a standalone aid for diagnosis. The phrase "computer analysis identifies the dental caries" suggests an algorithmic analysis.

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

   • "bitewings clichés or histological observation as a gold standard" were used. This indicates a combination of imaging (bitewings) and definitive histological examination (pathology).

7. The sample size for the training set:

   • Not specified. The document only refers to "in vitro and in vivo studies" and "bench tests" without providing sample sizes for any training that might have occurred for the "computer analysis."

8. How the ground truth for the training set was established:

   • Not specified. Given the lack of detail on a training set, the method for establishing its ground truth is also not provided.

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D-Carie is indicated for aiding in the diagnosis of dental carries

Similar to the Diagnodent the D-CARIE is indicated for use in aiding in the diagnosis of dental caries. The D-CARIE probe is similar in intended use, size, and shape to a Diagnodent probe tip. Identical to the technology in the Detectar the D-CARIE probe contains an optical fiber that reads the optical signature of suspicious areas and converts it into an electrical signal. From that electrical signal a computer analysis identifies areas that need further examination.

The provided 510(k) summary for the D-CARIE device offers limited details regarding specific acceptance criteria and the comprehensive study methodologies. However, based on the available information, here's an attempt to extract and describe the requested points:

The D-CARIE device is indicated for aiding in the diagnosis of dental caries.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: The document does not explicitly state the numerical sample size for any of the evaluations. It mentions "two in vitro and one in vivo evaluations" and "a third in vitro evaluation."
• Data Provenance: The studies were conducted in Canada, as the submitter, NEKS Technologies, is located in Laval, Quebec, Canada. The document states "Two in vitro and one in vivo evaluations... were conducted by experienced clinicians." It does not specify if these were retrospective or prospective studies, but "conducted by experienced clinicians" suggests a prospective setup for the clinical evaluations.

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

• Number of Experts: The document states that the evaluations were "conducted by experienced clinicians." It does not specify the exact number of experts involved in establishing ground truth for the test set.
• Qualifications of Experts: The experts are described as "experienced clinicians." Specific qualifications like "radiologist with 10 years of experience" are not provided. Given the nature of dental caries detection, these
  "clinicians" would likely be dentists.

4. Adjudication Method for the Test Set

The document does not provide details on the adjudication method used for the test set (e.g., 2+1, 3+1, none). It only mentions "conducted by experienced clinicians."

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

The document does not describe an MRMC comparative effectiveness study involving human readers with and without AI assistance. The comparisons mentioned are between D-CARIE and a predicate device (Diagnodent), not explicitly evaluating human reader improvement with D-CARIE.

6. Standalone Performance Study (Algorithm Only Without Human-in-the-Loop)

The information provided focuses on the device's performance in comparison to predicate devices, and its intended use is "aiding in the diagnosis." It mentions that the device "converts it into an electrical signal. From that electrical signal a computer analysis identifies areas that need further examination." This implies a degree of standalone algorithmic processing. However, the studies described don't explicitly isolate and report standalone performance metrics in a way that separates the algorithm's output from subsequent clinical interpretation or diagnostic decision-making. The "equivalence to Diagnodent" suggests comparing the overall diagnostic aid.

7. Type of Ground Truth Used

The document does not explicitly state the specific type of ground truth used (e.g., expert consensus, pathology, outcomes data). For dental caries studies, ground truth is often established through a combination of visual inspection, tactile examination (using dental explorers, which are themselves listed as predicate devices), and sometimes histological examination (pathology) for in vitro studies or follow-up over time for in vivo studies. Given the comparison to Diagnodent, which measures fluorescence, the ground truth likely involved a standard for identifying caries presence and severity.

8. Sample Size for the Training Set

The document does not provide any information regarding a training set sample size. This suggests that the device either did not undergo a distinct machine learning "training" phase in the modern sense or that this information was not deemed necessary for the 510(k) summary given the predicates.

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

Since no information is provided about a training set, there is no description of how ground truth for it was established.

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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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