在口内超声视频中对牙周复合物的自动评估
L Kumaralingam1, M H Hoang1, K C T Nguyen1
1Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada.
Journal of dental research
|December 18, 2025
概括
机器学习网络 OralSAM 在口内超声视频中自动化牙周结构评估. 这种人工智能工具可以提高牙周病管理的诊断准确性和一致性.
科学领域:
- 牙科和医学成像学 牙科和医学成像学
- 医疗保健中的人工智能
- 牙周病学 牙周病学
背景情况:
- 内腔超声波 (IUS) 提供了无侵袭的,无辐射的牙周结构的可视化.
- 精确评估膜骨水平 (ABL),牙厚度 (GT) 和膜骨厚度 (ABT) 对于诊断牙周疾病至关重要.
- 目前的手动评估方法耗时,主观,并限制实时临床应用.
研究的目的:
- 开发OralSAM,一个端到端的机器学习网络,用于在IUS视频中自动细分和定量评估牙周结构.
- 能够准确和一致地测量牙周关键参数,以改善诊断和治疗规划.
主要方法:
- 开发OralSAM,这是一个深度学习网络,用于在IUS视频中对牙,面膜,膜骨和水泥进行细分.
- 实施一种形态分析管道,用于提取临床相关的测量 (ABL,GT,ABT).
- 使用30名正牙患者的158个IUS视频进行验证,对专家注释和布兰德-阿尔特曼分析进行性能评估.
主要成果:
- 在关键牙周结构方面,OralSAM 实现了高细分精度.
- 来自OralSAM的定量测量表明了强大的评价者间可靠性,对ABL,GT和ABT达成一致的界限很窄.
- 对于ABL,GT和ABT的类内相关系数 (ICC) 分别为0.893,0.918和0.848.
结论:
- 口腔SAM准确地划出牙周结构,并从IUS视频中提供一致的定量评估.
- 人工智能框架显示了整合到常规牙科工作流程中的巨大潜力.
- 这项技术可以促进牙周病的早期检测,实时监测和个性化管理.
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