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深度学习分类性能用于诊断患有dentofacial形的患者的状骨关节炎,使用全景关节投影图像
Yukiko Iwase1, Tomoya Sugiki1, Yoshitaka Kise2
1Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, 2-11 Suemori-Dori, Chikusa-Ku, Nagoya, 464-8651, Japan.
Oral radiology
|July 11, 2024
概括
深度学习 (DL) 模型在患有牙脸形的患者中表现出强大的结节性骨关节炎 (OA) 诊断性能. 这些人工智能工具的表现优于牙科住院医生,提供了通过全景关节 (TMJ) 图像提高诊断准确性的潜力.
科学领域:
- 牙科 牙科是指牙科的专业.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 冠状骨关节炎 (OA) 是一种退行性关节疾病,影响关节 (TMJ).
- 准确诊断状骨关节炎对于管理牙面部形和关节疾病至关重要.
- 目前的诊断方法依赖于放射性解释,这种解释可能是主观的,准确性各不相同.
研究的目的:
- 评估深度学习 (DL) 模型的诊断一致性和性能,以评估状OA.
- 将DL模型的诊断能力与使用全景TMJ图像的牙科住院医生进行比较.
- 评估DL模型在诊断患有牙面部形症患者的状骨质炎症中的实用性.
主要方法:
- 开发了DL模型 (GoogLeNet,VGG-16) 在252个有和没有OA的TMJ上进行培训.
- 在使用常规全景图像 (Con-Pa),开口图像 (Open-TMJ) 和闭口图像 (Closed-TMJ) 来测试68个TMJ的DL模型.
- 将DL模型的性能 (AUC) 与两名牙科住院医生与不到一年的经验进行了比较.
主要成果:
- 在开放TMJ图像上,DL模型表现出中等到非常好的一致性.
- DL模型在Con-Pa和Open-TMJ图像上实现了明显更高的曲线下面面积 (AUC),而不是Con-Pa和Open-TMJ图像上的居民 (p < 0.01).
- 与封闭TMJ图像 (AUC=0.72) 相比,DL模型在开放TMJ图像 (AUC=0.89) 上表现更好.
结论:
- DL模型显示,在dentofacial变形患者中,状骨质炎的诊断准确度很高.
- DL模型可以作为一个有价值的工具,帮助牙科住院医生解读Con-Pa和Open-TMJ图像.
- 整合DL可以增强状骨关节炎的诊断过程,改善患者管理.
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