使用深度学习来评估根据希尔分类系统的胃食道门的功能
Zhenyang Ge1,2, Youjiang Fang3, Jiuyang Chang4
1Department of Gastroenterology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Annals of medicine
|November 10, 2023
概括
深度学习模型使用内镜图像准确地分类胃食道门 (GEFV) 功能,优于人类内镜师. 这种人工智能工具增强了用于GERD评估的Hill分类.
科学领域:
- 胃肠病学和消化内镜检查
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 胃食管 (GEFV) 的希尔分类对于评估食管胃口结 (EGJ) 功能至关重要.
- 深度学习 (DL) 在分析消化器内镜图像方面表现有前途.
- 提高希尔分类的准确性和效率对于例行内镜和GERD评估至关重要.
研究的目的:
- 开发一个DL模型用于GEFV的自动山坡分类.
- 使用注意力机制模块来增强DL模型的性能.
- 将DL模型的准确性与内镜师的性能进行比较.
主要方法:
- 一个由3256张GEFV内镜图像组成的数据集进行了策划.
- 评估了12个主流DL模型,其中ResNet-50,VGG-16,VGG-19和Xception被选中.
- 整合了注意力机制模块,以提高DL模型的性能.
主要成果:
- 在DL模型中,ResNet-50.0实现了0.989的曲线下的面积 (AUC).
- DL模型的分类准确度达到93.39%,明显超过初级 (74.83%) 和高级 (78.00%) 内镜医生.
- 带有注意力机制的DL模型表现出卓越的性能.
结论:
- 开发的DL模型在Hill分类方面表现出色.
- 这种人工智能工具具有显著的潜力,可以提高内镜师对希尔分类的准确性.
- 该模型可用于例行内镜报告和GERD评估检查.
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