Detection of spheno-occipital synchondrosis fusion stages using artificial intelligence.

Introduction: Accurate evaluation of the spheno-occipital synchondrosis (SOS) is important for growth assessment, early detection of craniofacial anomalies, and reliable forensic age estimation. Methods: This study applied three deep learning models-YOLOv5, YOLOv8, and YOLOv11-to cone-beam computed tomography (CBCT) sagittal images from 1,661 individuals aged 6-25 years, aiming to automate SOS fusion stage classification. Model performance was compared in terms of detection accuracy and processing speed. Results: All models achieved high accuracy, with a mean average precision (mAP) of 0.995 in complete fusion (Stage 3). YOLOv8 demonstrated the most consistent balance of precision and recall, while YOLOv11 achieved the fastest inference time (27 ms). YOLOv5 excelled in specific stages with perfect F1-scores. Discussion: These findings demonstrate that YOLO-based AI models can provide precise, rapid, and reproducible SOS assessments, offering valuable support for both clinical decision-making and forensic investigations.