深度学习在性结肠炎疾病严重程度的诊断和评估中的应用
Xinyi Jiang1,2, Xudong Luo3, Qiong Nan1,2
1Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
一个新的AI模型准确地评估性结肠炎 (UC) 内镜活动,并预测组织学缓解. 这种深度学习方法比人类内镜师提供了更客观的评估,改善了UC患者的护理.
科学领域:
- 胃肠病学 胃肠病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 性结肠炎 (UC) 治疗的目标是内镜和组织学缓解.
- 在评估中观察者间的变化可能会影响治疗结果.
- 对UC疾病活动的客观评估至关重要.
研究的目的:
- 开发一种深度学习算法,用于实时客观评估UC内镜疾病活动.
- 用人工智能预测UC患者的组织学缓解.
- 为了克服UC评估中观察者间变异性的局限性.
主要方法:
- 开发了两个卷积神经网络 (CNN) 模型,使用12,257张内镜图像和来自1124名UC患者的活检结果.
- 在梅奥内镜子分数 (MES) 和UC内镜重度指数 (UCEIS) 数据上训练模型.
- 在评估内镜活动和预测组织学缓解的准确性,灵敏性和积极预测价值方面评估模型性能.
主要成果:
- 在诊断内镜缓解时,MES-CNN模型实现了97.04%的准确性.
- 在评估内镜重度时,CNN模型表现出高精度 (MES-CNN为90.15%,UCEIS-CNN为85.29%).
- 在预测组织学缓解方面,CNN模型获得了91.28%的准确性,超过了人类内镜 (87.69%).
结论:
- 人工智能模型从内镜图像提供了UC炎症的精确评估.
- 人工智能方法可靠地预测组织学缓解.
- 这种人工智能工具增强了UC管理中的客观评估和预测.
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