慢性缩性胃炎的内镜诊断的U-Net深度学习模型和胃炎评估阶段的操作联系:一个前性嵌套病例控制研究
Quchuan Zhao1, Qing Jia2, Tianyu Chi3
1Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, Beijing, China.
Therapeutic advances in gastroenterology
|November 6, 2023
概括
深度学习模型准确诊断慢性缩性胃炎 (CAG),并使用胃炎评估操作链接 (OLGA) 系统识别高风险患者. 这种人工智能工具可以提高胃癌风险的内镜诊断和分层.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 胃炎评估操作链接 (OLGA) 系统对慢性缩性胃炎 (CAG) 患者的胃癌风险进行分层.
- 准确的诊断和分期对于管理CAG患者至关重要.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于对CAG和OLGA分期的实时内镜诊断.
- 与内镜医生相比,评估DL模型的诊断性能.
主要方法:
- 一项前性嵌套病例控制研究,涉及1306名患者.
- 开发基于U-Net的DL模型,用于自动标记缩病变和严重程度评估.
- 倾向性得分匹配以最大限度地减少偏差和DL模型和内镜师之间的诊断指数的比较.
主要成果:
- 在所有评估指数中,DL模型表现出比内镜师更好的诊断性能,包括灵敏度 (89.31%对67.56%),特异性 (90.46%对70.23%),准确度 (89.89%对68.89%).
- 对于DL模型,曲线下的面积 (AUC) 显著更高 (0.919与0.749相比,p < 0.001).
- 该模型在OLGA分期 (kappa = 0.816) 中与病理诊断取得了实质性的一致性.
结论:
- DL模型有效地协助内镜医生在胃镜检查期间实时进行CAG诊断.
- 该模型可以同步识别高风险的OLGA阶段患者 (III和IV),有助于胃癌风险分层.
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