DentoMorph-LDMs:基于新的适应性8连接牙组织和叶落牙损失的扩散模型,用于牙图像增强
Hanaa Salem Marie1, Mostafa Elbaz2, Riham Sobhy Soliman3
1Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa, 35712, Egypt. Hana.salem@deltauniv.edu.eg.
Scientific reports
|July 27, 2025
概括
新的人工智能模型通过保存牙解剖学和更准确地检测牙等疾病来改善儿科牙图像分析. 这些生物启发的算法提高了早期诊断,以改善儿童的口腔健康结果.
科学领域:
- 人工智能在牙科中的应用
- 儿科口腔健康成像 儿科口腔健康成像
- 医学图像分析 医学图像分析
背景情况:
- 儿童牙科图像分析受到缺少数据和独特的叶落牙发育的挑战.
- 现有的隐性扩散模型 (LDMs) 在儿科口腔结构中难以保持解剖完整性.
- 准确的重建对于诊断儿童牙发育中的疾病至关重要.
研究的目的:
- 在儿科牙科成像中为LDM开发新的,生物启发的损失功能.
- 改善儿科口腔结构的重建,保持解剖学和发育的准确性.
- 提高人工智能模型在儿童口腔疾病检测方面的性能.
主要方法:
- 引入了使用适应性像素社区模仿牙组织的牙适应性像素输入 (GAPI).
- 开发了基于树叶过渡的重建 (DTBR),考虑到初级牙过渡模式 (年龄为2-12岁).
- 将GAPI和DTBR集成到LDM中,在6个条件下对2,255张图像进行验证.
主要成果:
- 实现了优越的图像生成指标 (IS:9.87,FID:4.21,SSIM:0.952,PSNR:34.76),表现优于其他11种模型.
- 增强的数据集导致疾病检测模型的统计学上显著改善 (平均AP,精度,回忆,F1-Score).
- 在检测早期,发育异常和喷发障碍方面表现出更高的准确性.
结论:
- 具有GAPI和DTBR的新型LDM保留了儿科牙科图像中的结构连续性和年龄特征.
- 这个框架通过提供人工智能增强的成像工具来早期检测疾病,从而彻底改变了儿科牙科诊断.
- 为特定年龄的牙科人工智能应用奠定了基础,增强了儿科牙科的临床决策.
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