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构建一个人工智能辅助的系统,以评估基于山坡分类的胃食道功能 (附视频)
Jian Chen1, Ganhong Wang2, Kaijian Xia3
1Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, No. 1 Shuyuan Street, Suzhou, Jiangsu, 215500, China.
BMC medical informatics and decision making
|March 25, 2025
概括
一个人工智能模型,EfficientNet-Hill,已被开发用于胃食道门 (GEFV) 的Hill分类,达到83.32%的准确性. 这个AI工具帮助内镜医生提高GERD评估的诊断准确性.
科学领域:
- 胃肠病学和人工智能的人工智能
- 医学成像和诊断 医学成像和诊断
背景情况:
- 内镜希尔分类对于评估胃食道结 (EGJ) 形态至关重要.
- 胃食道 (GEFV) 的准确分类对于诊断像GERD这样的疾病至关重要.
研究的目的:
- 开发和部署人工智能 (AI) 模型,用于GEFV形态的自动Hill分类.
- 帮助内镜师在内镜分级过程中提高诊断效率和准确性.
主要方法:
- 一个深度学习模型 (EfficientNet-Hill) 在 1143 个 GEFV 图像和 17 个视频上使用 CNN 和 Transformer 架构开发.
- 转移学习,交叉损失,Adam优化器和学习速率调度用于模型训练.
- 模型的性能被评估使用准确度,精度,回忆,F1得分,并通过McNemar的测试与内镜师进行比较. 使用了可解释性技术 (t-SNE,Grad-CAM,SHAP).
主要成果:
- 效率网-希尔模型的准确度达到83.32%,超过初级内镜 (75.82%) 和高级内镜 (86.51%) 的表现.
- 人工智能模型在统计学上显示出比初级内镜医生 (p < 0.05) 的显著改善,与高级内镜医生 (p ≥ 0.05) 相比没有显著差异.
- 该模型在多个平台上实现了实时分类 (>50fps),确定了决策和错误分类分析的领域.
结论:
- 开发的EfficientNet-Hill AI模型使GEFV形态的自动Hill分类成为可能.
- 这种AI工具可以提高GERD评估的内镜分级的诊断效率和准确性.
- 便于将希尔分类纳入常规内镜报告和患者管理.
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