深度学习模型使用便图片来预测疹性结肠炎患者内性粘膜炎的预测
Jung Won Lee1, Dongwon Woo2, Kyeong Ok Kim3
1Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea.
The American journal of gastroenterology
|July 25, 2024
概括
使用便照片 (DLSUC) 的深度学习可以预测性结肠炎 (UC) 的内活动,类似于便calprotectin. 这种非侵入性工具有助于监测UC,显示出预测疾病复发的潜力.
科学领域:
- 胃肠病学 胃肠病学
- 人工智能在医学中的应用
- 医疗成像医学成像
背景情况:
- 性结肠炎 (UC) 的内活动可以影响便的特征.
- 开发了一种新的深度学习模型,DLSUC,用于分析便照片,以预测UC粘膜炎症.
研究的目的:
- 开发和验证一个深度学习模型 (DLSUC) 用于预测性结肠炎患者的内活动,使用便照片.
- 为了比较DLSUC与便calprotectin (Fcal) 在评估内镜炎症中的性能.
主要方法:
- 这是一项涉及6家三级医院的前性多中心研究.
- 患者在内镜检查前一周内提供了便照片.
- DLSUC在306名患者的2161张图像上进行了训练,并在126名患者的1047张图像上进行了验证,其性能与Fcal.com相比较.
主要成果:
- 对于预测内镜活动,DLSUC的AUC为0.801,相当于Fcal的AUC为0.837.
- 除了直肠节约病例,DLSUC的AUC提高到0.849,精度,灵敏度和特异性更高.
- 通过DLSUC分类为活跃的患者显示疾病复发的可能性更高 (P = 0.002).
结论:
- DLSUC在UC中预测内镜活动方面表现出显著的区分能力,与Fcal.相比.
- 在没有直肠节省的患者中,模型的准确性得到了改善.
- 通过DLSUC分析的便摄影为监测UC活动和潜在预测复发提供了一个有希望的,非侵入性的工具.
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