深度学习的临床应用,用于在全景放射图中增强多阶段虫检测
Suchaya Pornprasertsuk-Damrongsri1, Sirawich Vachmanus2, Dhanaporn Papasratorn3
1Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Mahidol University, 6, Yothi Road, Ratchathewi District, Bangkok, 10400, Thailand. suchaya.drs@mahidol.ac.th.
Scientific reports
|September 29, 2025
概括
这项研究使用深度学习在全景X射线上检测牙腐烂,实现高精度和回忆. 人工智能系统在帮助牙医诊断牙和规划治疗方面表现有前途.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 在全景射线影像上检测牙损往往是具有挑战性的和被忽视的.
- 准确的诊断对于有效的治疗计划和患者的结果至关重要.
研究的目的:
- 开发和评估一种深度学习系统,用于在全景放射图上识别多阶段牙损伤.
- 将深度学习系统的性能与人类专家诊断进行比较.
主要方法:
- 使用了500张全景放射图的数据集,其中有咬翼确认.
- 两种模型的深度学习方法:YOLOv5用于牙检测和注意力U-Net用于虫细分.
- 一位经验丰富的放射科医生对14,997颗牙中的1,792个病变进行了注释.
主要成果:
- 该系统在牙损伤计数和分类 (乳,牙,纸) 方面与牙医有很强的共识.
- 实现了0.96的高回忆率,优先考虑尽量减少错过的病变 (假阴性).
- 报告了F1得分为0.85和准确度为0.93的牙细分在后牙.
结论:
- 深度学习显示出有很大的潜力,可以帮助牙医从全景X射线图中诊断牙损伤.
- 开发的系统可以帮助治疗规划,牙科教育,改善诊断一致性.
- 需要进一步精细化,以解决低估了质损伤的问题,并减少了虚假阳性.
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