ArticleInternational dental journal2026
Student Performance in Bitewing Caries Detection: Artificial Intelligence Versus Alternative E-learning.
Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION AND
aimsThis study compared 3 methods for teaching caries detection in bitewings: a prerecorded lecture, a preannotated dataset, and an artificial intelligence (AI)-based web application.
methodsFifty-two dental students annotated carious lesions in 50 bitewings using minimum bounding boxes. After initial annotations, students were divided into 3 groups according to the training method: the Lecture Group (n = 16) received a prerecorded lecture on caries detection in bitewings, the Dataset Group (n = 17) had access to 50 bitewings annotated by a dentist, and the AI Group (n = 19) used an AI-based web application. After training, all students annotated caries in 50 previously unseen bitewings. Student annotations before and after training were compared to a reference standard of 3 experienced dentists. The evaluation was stratified according to the training method and stage of studies: preclinical (n = 16), junior clinical (n = 15), and senior clinical (n = 21).
resultsAll training methods significantly improved the mean number of errors, intersection over union of matching annotations, and accuracy. Sensitivity increased significantly in the Dataset Group (from 0.62 ± 0.14 to 0.78 ± 0.08) and the AI Group (from 0.68 ± 0.15 to 0.73 ± 0.12), as opposed to the Lecture Group, where a significant increase in specificity was observed (from 0.94 ± 0.09 to 0.96 ± 0.05). The stage of studies impacted the results; the extent of improvement decreased with increasing clinical experience.
conclusionWhile the 3 training methods varied in their impact on the confusion matrix components, they yielded comparable overall improvements. The AI-based web application could serve as an educational tool for caries detection in bitewings, especially for dental students with limited clinical experience. CLINICAL RELEVANCE: This study shows that learning bitewing caries detection with an AI tool yields improvements comparable to other tested e-learning methods. Evaluating and comparing established e-learning and AI teaching methods is key to optimising AI-assisted education for better learning outcomes in dental training.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.