自动检测,定位和近距离牙损的严重性评估从咬牙X射线图使用深度学习
Mashail Alsolamy1, Farrukh Nadeem1, Amr Ahmed Azhari2
1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22233, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|April 12, 2025
概括
一个新的深度学习系统可以从咬牙翼的X射线图中准确地检测和分级近牙,帮助牙医在诊断和规划这种常见的口腔感染的治疗.
科学领域:
- 人工智能在牙科中的应用
- 医学成像分析 医学成像分析
- 口腔健康信息学 口腔健康信息学
背景情况:
- 牙损是一种普遍的慢性感染,影响着相当一部分人口.
- 靠近性病由于其隐藏位置,难以早期检测,往往需要进行放射性评估.
- 咬翼射线图 (BR) 对于检测近位虫至关重要,但解释错误可能导致误诊.
研究的目的:
- 开发和评估一个深度学习系统,用于自动检测和严重程度分类来自BRs的近端牙.
- 为了使皮严重程度的评估与国际皮分类和管理系统 (ICCMS) 的指南保持一致.
- 为了提高诊断准确性和有效性,识别近位.
主要方法:
- 开发了一个使用预训练的YOLOv11实例分割模型的深度学习系统.
- 该系统执行三个核心任务:虫检测,牙编号和虫定位 (牙和表面).
- 一个由修复牙科顾问注释的1354个BR的数据集被用于培训 (80%),验证 (10%) 和测试 (10%).
主要成果:
- 该系统实现了高性能指标:精度为0.844,回忆率为0.864,F1得分为0.851,平均平均精度 (mAP) 为0.888,用于虫细分和严重程度分类.
- 在识别受影响的牙和牙表面时,在BR中实现了100%的准确性,牙完全或四分之三可见.
- 与手动牙医评估相比,该系统表现出更高的灵敏度和准确性.
结论:
- 拟议的深度学习系统显示出在协助牙医解读BRs时显著的前景.
- 它可以有效地帮助检测和评估近端牙的严重程度.
- 该系统有可能改善治疗规划和治疗的患者的治疗结果.
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