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Sultan Uzun¹, Guldane Magat¹, Cengiz Evli²

¹Department of Dentomaxillofacial Radiology, Necmettin Erbakan University Faculty of Dentistry, Konya, Türkiye.

Frontiers in physiology

|November 28, 2025

概括

深度学习模型从CBCT扫描中准确地分类了尾突综合症 (SOS) 融合阶段. YOLOv8和YOLOv11为临床和法医应用提供了出色的性能和速度.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
法医科学法医科学法医科学

背景情况:

精确评估尾同症 (SOS) 对于评估生长,识别面异常和法医年龄估计至关重要.
自动化SOS融合阶段分类可以提高这些评估的效率和准确性.

研究的目的:

应用和比较三个深度学习模型 (YOLOv5,YOLOv8,YOLOv11) 用于自动化的SOS融合阶段分类.
评估这些人工智能模型的检测准确度和处理速度在形束计算机断层扫描 (CBCT) 图像上.

主要方法:

利用CBCT从1661名年龄在6至25岁之间的个体的形图像.
训练并测试了YOLOv5,YOLOv8和YOLOv11模型,用于SOS融合阶段分类.
基于平均平均精度 (mAP),精度,回忆和推断时间等指标的模型性能比较.

主要成果:

所有YOLO模型都实现了高精度,完全融合 (第三阶段) 的mAP为0.995.
在精度和回忆方面,YOLOv8提供了平衡的性能.
YOLOv11以27ms的速度显示出最快的推断时间,而YOLOv5在特定阶段实现了完美的F1得分.

结论:

基于YOLO的AI模型提供了SOS融合的精确,快速和可重复的评估.
这些人工智能工具可以在增长评估和异常检测方面显著支持临床决策.
这些模型为可靠的法医年龄估计提供了宝贵的帮助.

关键词:

这是一个YOLO YOLO.人工智能的人工智能是人工智能.头骨脸部异常情况深度学习是一种深度学习.增长和发展的增长和发展.脊椎 - 头突突结合症

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