口腔解剖学知识为3D牙科CBCT细分和损伤检测提供半监督学习.
Yeonju Lee1, Min Gu Kwak1, Rui Qi Chen1
1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society
|September 8, 2025
概括
这项研究引入了一种新的AI模型,即口腔-解剖学知识知情半监督学习 (OAK-SSL),用于对3D牙科CBCT图像进行细分. 通过整合解剖学知识,OAK-SSL改善了病变检测,减少了大量手动标签的需要.
科学领域:
- 牙科成像和人工智能
- 医学图像分析 医学图像分析
- 医疗保健中的机器学习
背景情况:
- 圆束计算断层扫描 (CBCT) 在牙科医疗保健中对于诊断和治疗计划至关重要.
- 手动对3D CBCT图像进行细分是耗时的,需要专门的专业知识.
- 通过人工智能实现细分的自动化面临着挑战,因为需要大量的标记数据集.
研究的目的:
- 开发一种AI模型,用于自动化3D CBCT图像细分和病变检测.
- 解决牙科成像人工智能模型数据依赖性的局限性.
- 提高早期牙科疾病中病变检测的效率和准确性.
主要方法:
- 提出了一个新的口头-解剖学知识知情半监督学习 (OAK-SSL) 模型.
- 将定性口腔解剖学知识整合到深度学习框架中.
- 开发了基于知识的双重任务学习和半监督的损失功能.
主要成果:
- 与现有方法相比,OAK-SSL在细分3D CBCT图像方面表现出卓越的性能.
- 该模型有效地细分了小病变,这些病变在早期治疗中具有临床意义.
- 在现实数据集上取得了强大,准确和可概括的细分结果.
结论:
- OAK-SSL提供了一种有前途的方法来自动化3D CBCT细分和病变检测.
- 整合领域知识显著提高了AI模型在牙科成像中的性能.
- 这种人工智能驱动的方法可以提高牙科的诊断准确性和治疗规划.
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