一种基于双域分析和适应性注意力的新型智能牙炎症分级模型
Mengyun Chen1, Yue Dai1, Pengxiao Hu2
1Department of Periodontology, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 325027, China; Institute of Stomatology, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 325027, China.
Journal of dentistry
|February 13, 2026
概括
一种新的混合频自我注意检测变压器 (HFSA-DETR) 模型使用双域分析准确地评分牙炎症. 这种人工智能工具增强了早期检测,并为牙科诊所提供了定量评估.
科学领域:
- 人工智能在牙科中的应用
- 医学图像分析 医学图像分析
- 计算病理学计算病理学
背景情况:
- 检测牙炎症对于早期诊断和治疗至关重要.
- 精确分类微妙的炎症特征仍然是一个挑战.
- 不均的照明条件可能会阻碍牙图像的可靠分析.
研究的目的:
- 为牙炎症分级提出混合频率自我注意检测变压器 (HFSA-DETR) 模型.
- 为了提高检测微妙的炎症迹象和早期牙炎的准确性.
- 在具有挑战性的,不均的照明条件下增强模型灵敏度.
主要方法:
- 通过将空间频率混合变换模块集成到RT-DETR骨干中,开发了HFSA-DETR.
- 采用双域分析来捕捉牙炎的颜色,形态和纹理特征.
- 整合了一个自适应的稀疏自我注意力机制,以专注于关键区域并减少计算负载.
主要成果:
- 与基线相比,HFSA-DETR在内部数据集上的精度提高了3.9%,回忆提高了3.8%,mAP50提高了3.3%.
- 该模型显示,参数数量和计算要求显著减少.
- 在外部验证数据集上获得了92.8%的精度,91.5%的回忆率和93.2%的mAP50,显示出强大的通用性.
结论:
- 通过结合空间频率分析和适应性稀疏注意力,HFSA-DETR有效地对牙炎症进行分级.
- 该模型提供了一个具有成本效益的解决方案,用于客观的,定量化的牙炎症评估.
- 为牙医提供早期检测和干预的工具,支持临床决策,特别是在资源有限的环境中.
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