Authors
Wangyue Dai, Junyi Wu, Tongyu Li, Lingjuan Gong, Xiangning Chen, Yuxi Lei, Junping Li, Shan Gao, Zhen Lu, Baixiang Cheng, Cheng Chen
Published in
Journal of dentistry. Pages 106984. Aug 22, 2026. Epub Aug 22, 2026.
Abstract
To develop and internally evaluate a YOLO-based model for fine-grained localization and classification of dental trauma subtypes.
This retrospective single-center study used a CBCT-dominant dataset of 1,256 annotated trauma instances (1,065 instances derived from sagittal CBCT images and 191 instances derived from periapical radiographs) to develop and internally evaluate a YOLO26x object detection model. Performance was assessed using average precision (AP), [email protected], [email protected]:0.95, precision, recall, and inference speed.
AP was 90.4% for complicated crown-root fracture, 88.2% for uncomplicated crown fracture, and 83.9% for root fracture, but 27.3% for alveolar fracture and 48.9% for uncomplicated crown-root fracture. Overall [email protected], [email protected]:0.95, precision, and recall were 69.9%, 42.5%, 0.675, and 0.704, respectively; mean inference time was 3.3 ms per image.
The YOLO26x model showed subtype-specific performance in fine-grained detection of dental trauma, with better performance for injuries with clearer radiographic boundaries and lower performance for alveolar fracture and uncomplicated crown-root fracture. These internal findings support preliminary feasibility and warrant multicenter validation with patient-level separation.
If validated in multicenter studies with patient-level separation, AI-assisted image analysis could support lesion localization and subtype recognition when clinically indicated imaging is available; it should complement, rather than replace, comprehensive clinical assessment.
PMID:
42632476
Bibliographic data and abstract were imported from PubMed on 23 Aug 2026.
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