ArticleBMC oral health2024
Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study.
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 11 papers.
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11 citing papers in PubMed, 15 citations in OpenAlex.
- Automated measurement of Little's Irregularity Index on intraoral photographs using a convolutional neural network.BMC oral health · 2026Article
- Deep‑learning‑based detection of open‑apex teeth on panoramic radiographs using YOLO models.Oral radiology · 2026Article
- Deep Learning-Based Evaluation of Maxillary Dental Midline Deviation on Orthodontic Frontal Photographs.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
- Multi-Class Malocclusion Detection on Standardized Intraoral Photographs Using YOLOv11.Dentistry journal · 2026Article
- Evaluation of a YOLOv5x-based deep learning model for interproximal caries segmentation on bitewing radiographs across primary and permanent teeth.BMC oral health · 2025Article
- Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x.BMC oral health · 2025Article
- Automatic measuring of coronary atherosclerosis from medicolegal autopsy photographs based on deep learning techniques.Forensic science, medicine, and pathology · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Tooth-to-white spot lesion YOLO: a novel model for white spot lesion detection.BMC oral health · 2025Article
- Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images.Scientific reports · 2025Article
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Authors and funding
11 authors at 5 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future.
methodsA total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined.
resultsAmong the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425.
conclusionsThe model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm. CLINICAL REVELANCE: Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.
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