ArticleBMC oral health2025
Real-time segmentation and detection of ponticulus posticus in lateral cephalometric radiographs using YOLOv8: a step towards enhanced clinical evaluation.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Single-model deep learning approach for simultaneous cervical vertebral maturation staging and skeletal jaw relationship on lateral cephalograms using YOLOv8 and CNN.BMC oral health · 2026Article
- Transformer-enhanced vertebrae segmentation and anatomical variation recognition from CT images.Scientific reports · 2025Article
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4 authors.
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Abstract
objectivesPonticulus posticus (PP) is a bony structure in the cervical spine, often difficult to identify in radiographic images, and its detection is important for both orthodontic diagnosis and clinical decision-making related to craniovertebral pathologies. The purpose of this study is to develop a deep learning-based approach for detecting the PP in lateral cephalometric radiographs using the YOLOv8-seg model.
methodsThis retrospective study analyzed a dataset of 1000 anonymized lateral cephalometric radiographs, focusing on the segmentation and detection of the PP. Images were resized to 640 × 640 pixels and labeled by two experienced dentomaxillofacial radiologists. The YOLOv8-seg model, designed for segmentation tasks, was trained over 100 epochs with a batch size of sixteen, using pre-trained weights from the COCO dataset. Model performance was evaluated using precision, recall, mean average precision (mAP), and F1 score metrics.
resultsThe YOLOv8s-seg model demonstrated high accuracy in detecting the PP, with a precision of 62.81%, recall of 88.7%, mAP50 of 75.27%, mAP95 of 62.28%, and an F1 score of 73.54%. Even in cases where the boundaries of the C1 cervical vertebra were not clearly distinguishable, the model performed effectively, suggesting its reliability in clinical applications.
conclusionsThe proposed YOLOv8-seg model shows promising potential for improving the accuracy and efficiency of PP detection in lateral cephalometric radiographs. By integrating AI into the diagnostic process, orthodontic practices can benefit from more precise and reliable identification of small but clinically significant anatomical structures, ultimately enhancing patient care and diagnostic accuracy.
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