在内镜检查过程中通过可视化粘膜酸性损害来检测早期胃癌的AI辅助检测
Huihui Yan1, Zongkuo Li1, Jing Zhao2
1Department of Gastroenterology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 20, 2025
概括
一个人工智能系统可视化胃酸度以检测早期胃癌 (EGC),将其与胃炎区分开来. 这项技术有助于精确的定位和诊断,改善了内镜下层剖析指导.
科学领域:
- 胃肠病学 胃肠病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 早期胃癌 (EGC) 诊断是具有挑战性的,因为它与胃炎相似.
- 胃粘膜酸度的变化与癌前状况和EGC有关.
- 酸酶H+/K+运输β子单元 (ATP4B) 是酸分泌的关键调节者,在EGC下调.
研究的目的:
- 通过可视化胃粘膜酸度来开发和验证人工智能辅助的床边系统来检测EGC.
- 为改善EGC局部化,将粘膜 pH 地图与病理发现相关联.
- 评估系统预测炎症和帮助内镜手术的能力.
主要方法:
- 开发一种表面增强的拉曼散射 (SERS) 微阵列,用于粘膜 pH 映射.
- 使用患者数据进行多模型神经网络的培训和验证 (共1516分).
- 在独立的患者队列上进行外部验证,以评估诊断性能.
主要成果:
- 人工智能系统在pH测绘和病理图像之间取得了强烈的相关性 (R2 = 0.79).
- 外部验证显示了高灵敏度 (87.79%),特异性 (85.04%) 和准确性 (86.89%).
- 该系统检测到pH值的变化表明了缩性胃炎,肠道转化和EGC发病,预测了炎症.
结论:
- 人工智能辅助系统通过将pH映射与形态特征集成,使得精确的EGC定位成为可能.
- 这项技术改善了内镜下层剖析 (ESD) 的指导,并减少了假阳性诊断.
- 该系统显示了在最终的病理确认之前,早期炎症预测的潜力.
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