在CBCT扫描中,基于深度学习的3D自动细分受影响的狗在CBCT扫描中.
Türkan Ünal1, Alican Kuran2, Ibrahim Tevfik Gulsen3
1Alanya Oral and Dental Health Center, Antalya, Turkey.
BMC oral health
|December 24, 2025
概括
使用nnU-Net v2的新型深度学习模型可以在Cone Beam Computed Tomography (CBCT) 扫描中自动细分受影响的狗. 这种人工智能方法提高了牙面部放射学诊断效率.
科学领域:
- 牙大面部放射科 牙大面部放射科
- 人工智能在医学中的应用
- 医学图像分析 医学图像分析
背景情况:
- 受影响的狗是常见的牙异常,需要精确的定位来规划治疗.
- 在Cone Beam Computed Tomography (CBCT) 扫描上对受影响的狗进行手动细分是耗时的,容易变化.
- 深度学习为自动化和准确的细分提供了潜在的解决方案.
研究的目的:
- 开发和评估深度学习模型,用于在CBCT扫描中自动细分受影响的犬类.
- 为了评估 nnU-Net v2 架构对于这个特定应用的性能.
- 将模型的细分精度与已确定的指标进行比较.
主要方法:
- 追溯收集和注释159个CBCT扫描与受影响的犬.
- 使用nnU-Net v2架构训练一个深度学习模型.
- 使用分类指标 (回忆,精度) 和细分指标 (迪斯相似系数,豪斯多夫距离,与欧盟相交) 的性能评估.
主要成果:
- nnU-Net v2模型实现了0.90的回忆率和0.82的准确性,用于探测受影响的狗.
- 细分表现强,子相似系数 (DSC) 为0.84.
- 对于95%的豪斯多夫距离 (7.07毫米) 和交叉点 (0.74) 的有利结果表明了良好的细分精度.
结论:
- nnU-Net v2深度学习模型在CBCT卷中展示了受影响犬的有效和自主细分.
- 人工智能具有显著的潜力,可以提高牙口腔面部放射学的诊断效率.
- 开发的模型显示了临床应用在管理受影响的犬的承诺.
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