ArticleBMC oral health2024
Oral screening of dental calculus, gingivitis and dental caries through segmentation on intraoral photographic images using deep learning.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning for Detecting Dental Plaque and Gingivitis From Oral Photographs: A Systematic Review.Community dentistry and oral epidemiology · 2025Pooled it
- Impact of data augmentation and backbone architecture selection on dental caries segmentation in panoramic radiographs: a comparative deep learning study using pre-trained U-Net models.The Saudi dental journal · 2026Article
- MultiDentNet: a unified deep learning framework for multi-class dental condition screening and preliminary oral lesion triage.Scientific reports · 2026Article
- Determination of Fungiform Papilla Number Using Deep Learning Methods.Journal of imaging informatics in medicine · 2026Article
- Comparative evaluation of iRoot BP Plus and mineral trioxide aggregate for pulpotomy in primary molars with reversible pulpitis under intraoral endoscope: a retrospective study.BMC oral health · 2026Article
- Machine Learning-Based Transcriptomic Diagnosis of Periodontitis.International dental journal · 2026Article
- A deep learning approach to predict the results of root coverage procedures based on intraoral photographs: a retrospective study.BMC oral health · 2025Article
- Tooth-to-white spot lesion YOLO: a novel model for white spot lesion detection.BMC oral health · 2025Article
- Clinical validation of a deep learning based application for quantitative assessment of dental plaque in fluorescence imaging.Clinical oral investigations · 2025Article
- Clinical Applications of Artificial Intelligence in Periodontology: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- Data-driven AI platform for dens evaginatus detection on orthodontic intraoral photographs.BMC oral health · 2025Article
- Accuracy of digital photographs for assessing inflammatory gum disease in epidemiologic studies.Frontiers in oral health · 2025Article
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5 authors.
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Abstract
objectiveIntraoral photographic images are instrumental in the early screening and clinical diagnosis of oral diseases. In addition, people have been trying to apply artificial intelligence to these images. The purpose of this study is to investigate and evaluate a deep learning system designed to segment intraoral photographic images for the detection of dental caries, dental calculus, and gingivitis, and to assess the degree of dental calculus based on the overall features of the tooth surface and gingival margin. MATERIAL AND
methodsThis cross-sectional study collected 3,365 oral endoscopic images, randomly distributed in training datasets (2,019 images), validation dataset (673 images), and test dataset (673 images). The training set and verification set images are manually labeled. An oral endoscopic image segmentation method based on Mamba (Oral-Mamba) and an intelligent evaluation model of dental calculus degree were proposed, achieving the segmentation of two types of oral diseases, namely gingivitis and dental caries, as well as the segmentation of dental calculus regions, and the intelligent evaluation of the degree of dental calculus.
resultsOral-Mamba demonstrated high accuracy in segmentation, with accuracy rates for gingivitis, dental caries, and dental calculus at 0.83, 0.83, and 0.81, respectively. In particular, these rates surpassed those of the U-Net model in IoU, accuracy, and recall metrics. Furthermore, Oral-Mamba runs 25% faster than U-Net.The accuracy of degree classification in the intelligent evaluation model of dental calculus degree is 85%.
conclusionThe proposed deep learning system is expected to be used for the detection of two types of oral diseases and dental calculus, and the degree judgment of photographic images from an intraoral camera. This system offers a practical method to assist in the oral screening of dental caries, dental calculus, and gingivitis, providing benefits such as intuitive use, time efficiency, cost-effectiveness, and ease of deployment.
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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.