基于人工智能的牙损伤和牙数量检测在口腔内照片:模型开发和性能评估评估
Kyubaek Yoon1, Hye-Min Jeong2, Jin-Woo Kim3
1School of Mechanical Engineering, Yonsei University, Seoul, South Korea.
Journal of dentistry
|December 25, 2023
概括
这项研究开发了一种人工智能模型,用于在口腔内图像中自动编号牙和检测牙腐烂. 深度学习方法实现了高精度,为牙科诊断中的实际AI应用铺平了道路.
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
- 深度学习用于诊断.
背景情况:
- 精确的牙科诊断依赖于精确的牙识别和虫检测.
- 将这些任务整合到临床环境中可以提高效率和患者护理.
- 当前的人工智能解决方案往往专注于单一任务,限制了对口内图像的全面分析.
研究的目的:
- 开发和评估一个深度学习模型,用于同时识别牙号码和检测全口内图像中的牙腐烂.
- 为了评估模型的性能,使用大量数据集,对各种口内摄影视图进行评估.
- 为了证明AI驱动的集成牙科诊断在临床实践中的可行性.
主要方法:
- 使用基于级联区域的深卷积神经网络 (R-CNN) 模型.
- 在一个数据集上训练了该模型,该数据集包括 24,578 个口内图像,跨越了各种视图 (闭口,侧面,正面).
- 带有注释的图像与牙数量和牙损伤的边界框,包括位置和阶段.
主要成果:
- 实现了0.880的牙数识别平均平均精度 (mAP).
- 达到0.769的牙腐烂检测的平均mAP,得分范围从0.695到0.893.
- 在单一的深度学习框架内证明了这两个任务的成功整合.
结论:
- 开发的深度学习模型成功地将牙号码识别和虫检测集成到口腔内图像中.
- 该模型的性能表明其在临床工作流程中无集成的巨大潜力.
- 这种人工智能驱动的方法可以显著提升自动牙科诊断的实际应用.
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