从文本报告和全景放射图来分类牙科疾病的整体方法
Bernardo Silva1, Jefferson Fontinele2, Carolina Letícia Zilli Vieira3
1Intelligent Vision Research Lab, Institute of Computing, Federal University of Bahia, Bahia, Brazil; Federal Institute of Bahia, Bahia, Brazil.
Medical image analysis
|July 10, 2025
概括
这项研究引入了一种新的深度学习方法,用于分析牙全景放射图,而不需要标记牙数据. 该方法使用大型语言模型和图像细分来分类牙科疾病,实现接近专家的性能.
科学领域:
- 医疗成像医学成像
- 人工智能在牙科中的应用
- 计算病理学计算病理学
背景情况:
- 对于牙科全景放射分析的监督深度学习受到标记数据集稀缺性的限制.
- 从X光片中自动分析牙状况对于改善诊断至关重要.
研究的目的:
- 开发一个整体的学习框架,用于对全景放射图进行牙科疾病的分类,而无需直接注释牙水平.
- 利用大型语言模型和图像细分来自动识别牙状况.
主要方法:
- 利用大型语言模型从文本报告中自动标记牙疾病.
- 采用实例细分网络在全景射线图中对牙进行细分.
- 开发了一种链接方法,将细分的牙与从报告中识别的牙科疾病联系起来.
主要成果:
- 拟议的框架实现了超过基线马修斯相关系数的性能.
- 该系统的精度与牙科最后一年的学生相美.
- 根据专家的共识验证时,表现在专家表现的8.1个百分点内.
结论:
- 整体学习方法有效地在全景放射图上对牙科疾病进行分类,使用未标记的数据.
- 这种方法为克服人工智能驱动的牙科诊断数据限制提供了一个有希望的解决方案.
- 该框架显示了协助牙科专业人员解释全景放射图的潜力.
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