基于多模式深度学习的查退行性关节疾病使用2D放射:一个成本效益和低辐射的方法
Xin Xiong1, Ming Yang1, Yunhao Zheng1
1State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Oral diseases
|March 7, 2026
概括
一个新的深度学习模型有效地使用二维X射线选 temporomandibular关节 (TMJ) 退行性关节疾病 (DJD). 综合全景和脑表成像可以提高这种常见疾病的诊断准确度.
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) 退行性关节疾病 (DJD) 影响了许多患者.
- 准确且易于使用的查方法对TMJ DJD至关重要,以便及时进行干预.
- 目前的查通常依赖于更复杂的成像模式.
研究的目的:
- 开发和验证用于TMJ DJD查的多式联络深度学习模型.
- 为了利用易于获得的2D大面部成像.
- 为了评估模型的性能,使用圆束计算机断层扫描作为黄金标准.
主要方法:
- 从500名正牙患者中对1000个TMJ进行了回顾性分析.
- YOLOv8模型用于全景射线图上的尾位.
- EfficientNetV2网络用于从全景射线图和横向脑电图中提取特征.
- 图像特征与患者年龄和性别的融合用于分类.
主要成果:
- YOLOv8在体定位方面获得了高精度 (mAP50=0.995).
- 多模式模型显示强的查性能 (AUC=0.898),具有良好的灵敏度和特异性.
- 组合的2D成像模式仅仅超过了全景放射,Grad-CAM证实了对相关结构的关注.
结论:
- 多式联动深度学习模型提供有效,低辐射和成本效益的TMJ DJD查.
- 整合全景和横向脑表成像可以提高查准确度.
- 为了更广泛的验证,建议进行进一步的多中心研究.
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