Evidence map›Paper›PMID 42050285›Full record

ArticleClinical oral investigations2026

Evaluating AI diagnostic accuracy in approximal dental caries detection on bitewing radiographs.

Kostis Giannakopoulos, Argyro Kavadella, Dimitrios Paraskevis, Aristidis Arhakis, Miltiadis A Makrygiannakis, Eleftherios G Kaklamanos

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Article in Clinical oral investigations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Kostis GiannakopoulosSchool of Dentistry, European University Cyprus, 6 Diogenous str, Nicosia, 2404, Cyprus. k.giannakopoulos@external.euc.ac.cy.
Argyro KavadellaSchool of Dentistry, European University Cyprus, 6 Diogenous str, Nicosia, 2404, Cyprus.
Dimitrios ParaskevisSchool of Dentistry, European University Cyprus, 6 Diogenous str, Nicosia, 2404, Cyprus.
Aristidis ArhakisSchool of Dentistry, Aristotle University of Thessaloniki, Aristotle University of Thessaloniki Campus, Thessaloniki, 54124, Greece.
Miltiadis A MakrygiannakisSchool of Dentistry, European University Cyprus, 6 Diogenous str, Nicosia, 2404, Cyprus.
Eleftherios G KaklamanosSchool of Dentistry, European University Cyprus, 6 Diogenous str, Nicosia, 2404, Cyprus.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the diagnostic accuracy of the Diagnocat™ artificial intelligence (AI) system for caries detection on bitewing radiographs compared with expert human examiners, with emphasis on differences between enamel and dentin lesions. MATERIALS AND

methodsA sample of 100 digital bitewing radiographs (1540 surfaces) was retrospectively selected from the European University Cyprus dental clinic database using a systematic backward screening method. Radiographs were obtained with a standardized phosphor plate system and anonymized before analysis. Two independent experts (operative dentistry and oral radiology) established the reference standard. AI and human assessments were binarized (caries/no caries; enamel/dentin) and compared. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated, and statistical significance was tested across detection categories.

resultsDiagnocat™ showed high specificity (94.3%, 95% CI: 92.4%–96.0%) and NPV (96.1%, 95% CI: 94.7%–97.3%), with an overall accuracy of 91.6% (95% CI: 89.7%–93.4%). Sensitivity was moderate (73.1%, 95% CI: 65.9%–79.9%), and PPV was 64.7% (95% CI: 57.7%–71.5%). Agreement with the expert consensus was substantial (Cohen’s κ = 0.638). For enamel lesions, sensitivity and specificity were 73.3% (95% CI: 62.8%–82.7%) and 92.9% (95% CI: 91.0%–94.7%) with moderate agreement with the consensus (Cohen’s κ = 0.492) and for dentin lesions they were 72.8% (95% CI: 61.8%–83.8%) and 92.8% (95% CI 90.9%–94.6%) with moderate agreement with the consensus (Cohen’s κ = 0.468). NPV remained high (≥ 98.0%), while PPV was low (42.0% and 39.2%), across lesion types. Detection patterns differed significantly between AI and the reference standard (p < 0.001).

conclusionsDiagnocat™ demonstrated good diagnostic performance in ruling out caries. However, its overall lower sensitivity emphasizes the need for clinician oversight, especially in detecting early-stage disease. CLINICAL RELEVANCE: This study offers an independent validation of Diagnocat™ using bitewing radiographs. It demonstrates lesion-depth–specific insights, showing that while AI is highly reliable for excluding disease, its predictive value remains limited.

Indexed as

Artificial IntelligenceDental CariesRadiography, BitewingHumansIntelligent SystemsPredictive Value of TestsRetrospective StudiesSensitivity and SpecificityArtificial intelligenceBitewing radiographsCaries detectionComputer-aided diagnosisDiagnostic accuracy

Identifiers

PMID42050285
PMCPMC13124809

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