在下牙中,牙根 (radix entomolaris) 的形态型分层
Siva Shankar Dev1, Ramya Ramadoss1, K Nitya1
1Department of Oral Biology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600077, India.
Journal of oral biology and craniofacial research
|October 27, 2025
概括
在牙中,牙内 (Radix entomolaris,RE) 带来了内的挑战. 圆束CT和机器学习有效地分类根管解剖学,改善复杂病例的治疗计划.
科学领域:
- 牙周内科医院 牙周内科医院 牙周内科医院
- 牙科解剖学 牙科解剖学
- 放射学 放射学是一门学科.
- 在牙科领域的机器学习
背景情况:
- 牙内 (Radix entomolaris,RE) 是下牙中的一个超数根,由于其复杂的解剖结构,这给牙内治疗带来了重大挑战.
- 传统的放射学往往无法检测RE,增加了错过通道和治疗失败的风险.
研究的目的:
- 用圆束计算断层扫描 (CBCT) 来描述RE的形态复杂性.
- 开发机器学习 (ML) 模型来分类根运河形态型和预测RE中的解剖分叉.
主要方法:
- 有RE的100个下第一个牙经历了高分辨率的CBCT扫描.
- 形态参数 (通道曲率,面积,圆度,体积,根融合) 被提取并用于训练ML模型.
- 一个决策树分类器预测了分叉,K-意味着聚类分层形态型.
主要成果:
- 在预测分叉时,ML模型实现了高准确性 (F1得分为0.87).
- 体积运河大小是分叉的最强预测因素 (AUC=0.81).
- 确定了两种形态型:简单 (圆形) 和复杂 (不规则/分叉/C形);27%显示中根分叉.
结论:
- 从CBCT衍生出来的特征,特别是通道体积和曲率,有效地预测了RE复杂性.
- 整合这些特征的ML模型为个性化内牙科规划提供了一个框架.
- 人工智能辅助诊断可以改善复杂根管解剖学的管理.
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