Evidence map›Paper›PMID 41108479›Full record

ArticleOral radiology2026

Assessment of posterior interproximal surfaces on bitewing and panoramic radiographs by dental interns and an artificial intelligence application.

Emre Köse, Sunay Hoşgör

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Oral radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

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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

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Emre KöseDepartment of Oral and Maxillofacial Radiology, Faculty of Dentistry, Aydın Adnan Menderes University, Aydın, 09010, Turkey.ORCID 0000-0002-0659-7157
Sunay HoşgörDepartment of Oral and Maxillofacial Radiology, Faculty of Dentistry, Aydın Adnan Menderes University, Aydın, 09010, Turkey. sunayhosgor@gmail.com.ORCID 0009-0002-4810-109X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe aim of this study was to evaluate the interproximal surfaces of posterior teeth using panoramic and bitewing radiographs, and to compare the diagnostic accuracy of dental interns with that of an artificial intelligence application.

methodsInterproximal surfaces were evaluated on 11 panoramic radiographs and 22 bitewing radiographs obtained from 11 patients. The evaluation focused on the presence and depth of caries on interproximal surfaces without restorative fillings, and on the detection of secondary caries and overhanging dental restorations on restored surfaces. Panoramic and bitewing radiographs were evaluated by 20 fourth- and 20 fifth-year dental interns, and CranioCatch.

resultsBitewing radiographs were significantly more effective than panoramic radiographs in detecting interproximal caries, caries depth, secondary caries, and overhanging restorations (p < 0.001). In the detection of secondary caries on bitewing radiographs, the artificial intelligence application performed worse than both dental intern groups, and this difference was statistically significant (p = 0.033). In determining caries depth on panoramic and bitewing radiographs, the artificial intelligence application yielded numerically higher diagnostic accuracy values compared to the dental intern groups, but these differences were not statistically significant.

conclusionOur study concluded that the diagnostic accuracy of the artificial intelligence application in evaluating the interproximal surfaces of posterior teeth was comparable to dental interns.

Indexed as

Artificial IntelligenceDental CariesInternship and ResidencyRadiography, PanoramicAdultFemaleHumansMaleArtificial intelligenceBitewing radiographsInterproximal cariesOverhanging restorationsSecondary caries

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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.