在CT图像中自动检测结石的AI驱动框架:集成深度学习架构和变压器
Reem Alshenaifi1, Yahya Alqahtani2, Shabnam Mohamed Aslam1
1Department of Information Technology, College of Computer Sciences and Information Technology, Majmaah University, Majmaah, 11952, Saudi Arabia.
Biomedical physics & engineering express
|July 24, 2025
概括
本研究引入了一种人工智能驱动的方法,用于CT扫描中的自动结石检测和分类. 开发的模型显示高精度,提高了泌尿病的诊断效率.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 结石是一种常见的泌尿器科问题,会引起严重的疼痛.
- 计算机断层扫描 (CT) 是诊断的标准,但手动解释是缓慢的,容易出错.
研究的目的:
- 开发一种基于人工智能的方法,用于CT图像中自动检测和分类结石.
- 提高诊断准确性和管理结石的效率.
主要方法:
- 探索各种机器学习 (ML) 和深度学习 (DL) 架构.
- 实施了两阶段的工作流:DL用于兴趣地区 (ROI) 分段和ML/DL用于分段ROI的二进制分类.
主要成果:
- 在细分方面,SwinTResNet实现了97.27%的培训和96.16%的验证准确性.
- 视觉变压器 (ViT) 在分类中获得了96.63%的培训和95.67%的验证准确性.
结论:
- 综合人工智能框架显示了改善结石诊断的巨大潜力.
- 这种方法可以提高临床决策和结石患者的管理.
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