Evidence map›Paper›PMID 40638221›Full record

Trial reportDento maxillo facial radiology2025

Machine learning algorithms enhance the accuracy of radiographic diagnosis of dental caries: a comparative study.

Shwetha Hegde, Jinlong Gao, Stephen Cox, Shanika Nanayakkara, Rena Logothetis, Rajesh Vasa

Abstract readComparative StudyRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Dento maxillo facial radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Photonic Methods for the Assessment of Lesion Activity.Diagnostics (Basel, Switzerland) · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Shwetha HegdeDentomaxillofacial Radiology, Sydney Dental School, University of Sydney, Sydney, 2010, Australia.ORCID 0000-0003-1429-9149
Jinlong GaoSydney Dental School, Institute of Dental Research, Westmead Centre for Oral Health, University of Sydney, Sydney, 2145, Australia.ORCID 0000-0002-5153-5393
Stephen CoxDiscipline of Oral Surgery, Sydney Dental School, University of Sydney, Sydney, 2010, Australia.
Shanika NanayakkaraSydney Dental School, Institute of Dental Research, Westmead Centre for Oral Health, University of Sydney, Sydney, 2145, Australia.ORCID 0000-0002-5479-9650
Rena LogothetisApplied Artificial Intelligence Institute, Deakin University, Melbourne, 3125, Australia.ORCID 0000-0003-0143-3812
Rajesh VasaTranslational Research and Development, Applied Artificial Intelligence Institute, Deakin University, Melbourne, 3125, Australia.ORCID 0000-0003-4805-1467

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study evaluated the influence of cognitive aids, including machine learning (ML) algorithms and checklists, on the diagnostic accuracy and confidence of dental students in detecting dental caries on bitewing radiographs.

methodsFifty-two third-year dental students were randomly assigned to control, ML, or checklist groups. The participants recorded their caries diagnoses (charting) on 10 bitewing radiographs and rated their confidence. Diagnostic accuracy and reliability were compared between groups for caries detection (present/absent). The inter-rater reliability for International Caries Detection and Assessment System II (ICDAS II) caries grading was assessed using weighted kappa. Participants also completed questionnaires on their perceptions of cognitive aids.

resultsML group showed the highest diagnostic accuracy and confidence levels. For caries detection, the ML group achieved the highest sensitivity (79%) and diagnostic odds ratio (20.3), while the checklist group had the highest specificity (90.9%) (P < .001). The control group showed moderate sensitivity (67.9%) but outperformed the checklist group in this metric. Inter-rater agreement for caries detection was highest in the ML group (κ = 0.702, 95% CI: 0.692-0.713; P < .001), followed by the checklist group. The ML group also had the highest weighted kappa for ICDAS II grading (κ = 0.520, P < .001). Confidence levels differed significantly between groups (P < .001), with the ML group reporting the highest confidence.

conclusionML algorithms may enhance diagnostic accuracy and confidence, possibly by reducing cognitive load. While standardizing the diagnostic process, checklists were less effective, likely due to their lack of flexibility and clinical context. Further research is needed to better understand our findings and translate them into clinical workflows.

Indexed as

AlgorithmsDental CariesMachine LearningRadiography, BitewingAdultChecklistClinical CompetenceFemaleHumansMaleReproducibility of ResultsSensitivity and SpecificityStudents, Dentalbitewing radiographychecklistscognitive aidsdental caries diagnosismachine learning

Identifiers

PMID40638221
PMCPMC12653770

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.