将领域知识与深度学习结合起来,对识别良性和恶性胃白色病变的影响:一项回顾性研究
Xiaoquan Zeng1,2,3,4, Lang Yang5, Zehua Dong1,2,3,4
1Renmin Hospital of Wuhan University, Wuhan, China.
Journal of gastroenterology and hepatology
|February 28, 2024
概括
一个新的深度学习系统,ENDOANGEL-WD,通过整合领域知识,有效地诊断胃白色瘤. 与传统的深度学习模型和人类内镜医生相比,这种方法显著提高了诊断准确性.
科学领域:
- 胃肠病学 胃肠病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 早期的白色胃瘤是难以准确诊断的挑战.
- 胃白色病变的差异诊断仍然是一个重要的临床障碍.
研究的目的:
- 开发一种深度学习 (DL) 模型,用于诊断胃白色瘤.
- 调查将域名知识纳入DL模型构建中的影响.
主要方法:
- 训练并测试了DL模型,使用来自两个机构的4558张图像.
- 开发基于文献识别的诊断特征 (边界,表面,圆度,低谷,位置) 的特征提取模型.
- 将DL预测与提取的特征结合到机器学习模型中,选择最佳的 ENDOANGEL-WD.
主要成果:
- 与个人DL模型和10名内镜师 (80.47%对75.70%,P<0.001) 相比,开发的ENDOANGEL-WD系统显示出更高的诊断准确性.
- 将领域知识纳入DL模型显著提高了具体性和准确性.
- ENDOANGEL-WD实现了比单一DL模型 (84.38%与72.27%,P<0.05) 更高的特异性,比另一个单一DL模型 (80.47%与68.67%,P<0.001) 更高的准确性.
结论:
- 一个新的系统,ENDOANGEL-WD,是通过将领域知识与传统DL结合起来开发的,用于胃白色瘤检测.
- 整合领域知识可以提高DL模型的性能,用于诊断罕见的胃病.
- 这种方法为开发其他罕见疾病的诊断模型提供了有希望的解决方案.
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