基于深度学习的CBCT辅助诊断系统用于关节椎间盘位移,基于深度学习
Yijiao Fu1, Jiayi Li1, Qianglan Zhai1
1Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Shanghai Research Institute of Stomatology, Shanghai, China.
Progress in orthodontics
|February 9, 2026
概括
这项研究开发了一种深度学习模型,使用圆束计算机断层扫描 (CBCT) 图像选关节 (TMJ) 盘位移,显示其作为初步诊断工具的潜力.
科学领域:
- 牙科和口腔卫生 牙科和口腔卫生
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 关节 (TMJ) 盘位移的诊断依赖于MRI,这是昂贵和耗时的.
- 圆束计算断层扫描 (CBCT) 显示间接迹象,但解释需要专业知识.
- 深度学习为高效的TMJ盘位移查提供了一个潜在的解决方案.
研究的目的:
- 开发和评估一种深度学习模型,用于从CBCT图像中预测TMJ盘位移风险.
- 为了提高诊断TMJ盘位移的可访问性和效率.
主要方法:
- 使用330名患者的CBCT图像创建了一个两阶段的深度学习模型.
- 一个物体检测模型 (YOLOv11) 确定了TMJ感兴趣的区域 (ROI).
- 一个二进制分类模型 (FastViT-t8) 预测了磁盘位移,与放射科医生MRI诊断进行了验证.
主要成果:
- 对象检测模型实现了高精度 (0.986) 和回忆 (0.982).
- 该分类模型显示AUC为0.733,准确度为0.669.
- 这些结果表明TMJ盘位移的初步查能力.
结论:
- 深度学习模型显示,作为使用CBCT进行TMJ盘位移的初步选工具,它具有前途.
- 它可以作为牙矯正的分拣工具,识别需要进一步的MRI的患者.
- 该模型为未来人工智能辅助牙科诊断研究提供了基础.
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