ArticleScientific reports2025
CNN-based remote dental diagnosis model for caries detection with grad-CAM.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled 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.
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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable artificial intelligence in dental imaging: a systematic review of interpretability and the current state of trust evidence.Frontiers in dental medicine · 2026Pooled it
- DentSeg3: A Three-Branch Specialized Fusion Network for Dental Lesion Segmentation in Color Images.Journal of imaging informatics in medicine · 2026Article
- Deep learning models for the detection of dental-findings and tooth-types using video data.BMC oral health · 2026Article
- Sex estimation from lateral cephalograms via a hybrid multimodel convolutional neural network.Scientific reports · 2026Article
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- ACO-optimized MobileNetV2-ShuffleNet hybrid model for automated dental caries classification.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Dental caries is a prevalent global condition, and its diagnosis often requires direct clinical examination by a dentist. However, access to traditional dental care can be limited due to high costs, availability, and patient discomfort. To address these limitations, this study introduced a remote caries detection model using a ResBlock-AutoEncoder that generates domain-specific pre-trained weights. The model demonstrated exceptional performance, achieving an accuracy of 0.9989, an F1-score of 0.9979, and a precision of 1.0, while maintaining a low average inference time of 5.7939 seconds. Furthermore, Grad-CAM was employed to enhance interpretability by visually localizing caries, ensuring model reliability. Notably, this high precision is attributed to the specific characteristics of frontal oral images, which allow for clearer visibility of caries compared to other imaging angles. However, this also introduces a potential limitation, as it does not account for variability in other perspectives of oral images. To improve generalization, future research will incorporate multi-angle dental images.
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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.