在公共卫生查中,用于自动检测口腔疾病的新型元启发式优化隐性扩散框架
Marwa Sabry1, Mostafa Elbaz2, Waleed Obaid Alzabni1
1Faculty of Dentistry, Kafrelsheikh University, Kafrelsheikh, Egypt.
Scientific reports
|November 18, 2025
概括
这项研究介绍了DentoSMART-LDM,这是一种用于增强牙X射线和改善罕见口腔疾病检测的新型AI框架. 它显著提高了诊断的准确性和信心,即使数据有限.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 口腔病理学 口腔病理学
背景情况:
- 自动口腔疾病检测与放射性质量差以及罕见疾病数据有限的情况作斗争.
- 现有的AI系统缺乏同时解决图像退化和数据稀缺问题的能力.
研究的目的:
- 推出DentoSMART-LDM,这是第一个整合元启发性优化和牙科成像潜在扩散模型的框架.
- 解决图像质量和数据稀缺方面的挑战,用于自动检测口腔疾病.
主要方法:
- 开发了DentoSMART-LDM,将放射性牙增强 (DSMART) 的动态自适应多目标元启发算法与具有病理意识的潜在扩散模型 (DentoLDM) 结合起来.
- 通过使用自适应性搜索平衡五个质量指数,DSMART优化了牙科放射.
- 在合成数据生成过程中,DentoLDM结合了病理特异性的注意机制,以确保诊断完整性.
主要成果:
- DentoSMART-LDM实现了卓越的图像增强 (SSIM 0.941,PSNR 34.82 dB),其性能比竞争方法高出9.0%和11.5% (p < 0.001).
- 在增强数据上训练的诊断模型的整体准确率达到97.3%,专家评估有显著的改进 (准确率+17.4%,信心+23.4%).
- 证明了特殊的少数射击学习性能 (89.2%准确度,每种病理有2个样本).
结论:
- 通过整合优化和生成模型,DentoSMART-LDM提供了牙科AI的范式转变.
- 该框架有效地平衡了增强质量,诊断保存和计算效率.
- 它为罕见的口腔病理提供了前所未有的短时间学习能力,使服务不足的社区受益.
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