ArticleBiomedical optics express2023
Deep learning and sub-band fluorescence imaging-based method for caries and calculus diagnosis embeddable on different smartphones.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed, 12 citations in OpenAlex.
- Diagnostic accuracy and feasibility of artificial intelligence-driven smartphone imaging for dental caries detection: A systematic review.The Japanese dental science review · 2026Review
- Artificial Intelligence and Innovation in Oral Health Care Sciences: A Conceptual Review.Healthcare (Basel, Switzerland) · 2025Review
- Tooth-to-white spot lesion YOLO: a novel model for white spot lesion detection.BMC oral health · 2025Article
- Clinical Applications of Artificial Intelligence in Periodontology: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- SegmentAnyTooth: An open-source deep learning framework for tooth enumeration and segmentation in intraoral photos.Journal of dental sciences · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review.APL bioengineering · 2024Review
- The Use of Artificial Intelligence in Caries Detection: A Review.Bioengineering (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Popularizing community and home early caries screening is essential for caries prevention and treatment. However, a high-precision, low-cost, and portable automated screening tool is currently lacking. This study constructed an automated diagnosis model for dental caries and calculus using fluorescence sub-band imaging combined with deep learning. The proposed method is divided into two stages: the first stage collects imaging information of dental caries in different fluorescence spectral bands and obtains six-channel fluorescence images. The second stage employs a 2-D-3-D hybrid convolutional neural network combined with the attention mechanism for classification and diagnosis. The experiments demonstrate that the method has competitive performance compared to existing methods. In addition, the feasibility of transferring this approach to different smartphones is discussed. This highly accurate, low-cost, portable method has potential applications in community and at-home caries detection.
Identifiers
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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.