一种新的双嵌入的少数射击学习方法,用于分类骨损失,使用骨透视图的放射性笔记
Pradeep Kumar Yadalam1, Amit Rajabhau Pawar1, Prabhu Manickam Natarajan2
1Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, 600077, Tamil Nadu, India.
Head & face medicine
|July 10, 2025
概括
一个新的DualFit模型准确地从牙科X射线笔记中分类骨损失严重程度,提高诊断效率. 这种人工智能方法有助于早期检测牙周病和全身疾病.
科学领域:
- 人工智能在牙科中的应用
- 用于医学成像的自然语言处理.
- 机器学习用于检测牙周病的检测.
背景情况:
- 整形眼镜 (OPG) 对于诊断牙和大面部疾病至关重要,包括 parodontal 疾病和骨质疏松症的标志性骨损失.
- 对OPG放射性笔记的自动分析可以提高诊断准确度并减少观察者之间的变化.
- 基于变压器的模型提供了从临床文本分类骨损失严重程度的潜力.
研究的目的:
- 从Orthopantomogram (OPG) 放射性笔记中对骨损失严重程度进行分类 (正常,轻度,严重).
- 为此分类任务开发和评估一种新的双嵌入式少量学习框架.
主要方法:
- 利用OPGs的放射性笔记数据集,使用Glickman的分类系统对骨损失进行分类.
- 开发了 DualFit 模型,具有单独的文本和特征处理分支,包含批量规范化和丢弃.
- 采用了融合层来整合来自两个分支的输出,以优化分类性能.
主要成果:
- 与BioBERT,ClinicalBERT和PubMedBERT相比,DualFit模型获得了更高的性能.
- 获得了高精度 (98.98%),精度 (98.71%),回忆 (99.14%) 和F1得分 (98.92%),比PubMedBERT.增加了5.53%的精度.
- 证明了出色的多类分类,准确度,回忆和AUC值近乎完美.
结论:
- 双合适模型代表了对牙周骨损失的OPG笔记的自动分类的重大进步.
- 这种模式简化了诊断,减少了放射科医生的工作量,并促进了及时的患者干预.
- 未来的研究应该集中在外部验证和与多式联络诊断系统的集成上.
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