Detection of spheno-occipital synchondrosis fusion stages using artificial intelligence

TL;DR

This study uses AI to accurately and quickly assess the fusion stages of the spheno-occipital synchondrosis in CT images, aiding clinical and forensic evaluations.

Contribution

The novel application of YOLO-based models for automated SOS fusion stage classification in CBCT images is presented.

Findings

All models achieved high accuracy, with a mean average precision of 0.995 for complete fusion.

YOLOv8 balanced precision and recall best, while YOLOv11 had the fastest inference time at 27 ms.

YOLOv5 achieved perfect F1-scores in specific fusion stages.

Abstract

Accurate evaluation of the spheno-occipital synchondrosis (SOS) is important for growth assessment, early detection of craniofacial anomalies, and reliable forensic age estimation.

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.

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.

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.

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