深度学习模型用于区分梅奥内镜子评分0和1的性结肠炎患者
Ji Eun Kim1, Yoon Ho Choi2,3, Yeong Chan Lee4
1Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, South Korea.
深度学习模型在性结肠炎 (UC) 患者中准确区分梅奥内镜子分值 (MES) 0和MES 1. 这种AI工具提高了UC内镜评估的诊断精度.
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:
- 精确的内镜评估对于治疗性结肠炎至关重要.
- 在梅奥内镜分值 (MES) 0 (不活跃疾病) 和MES 1 (轻度疾病) 之间进行区分可能具有挑战性.
- 客观差异化有助于治疗决策和监测疾病进展.
研究的目的:
- 开发和验证一种深度学习模型,以区分UC患者的MES 0和MES 1.
- 用内部和外部数据集评估模型的性能.
- 与传统分类方法相比,评估模型的优势.
主要方法:
- 设计了一个基于卷积神经网络 (CNN) 的深度学习模型.
- 该模型使用了来自492名UC患者的984张结肠和直肠图像.
- 该模型包含了结肠和直肠的辅助分类器,以及最终的联合MES分类器.
主要成果:
- 在内部测试中,F1得分为0.92,AUROC为0.97,AUPRC为0.98.
- 该模型的表现优于七个新手分类器及其共识.
- 在Hyperkvasir数据集的外部测试中,F1得分为0.89,AUROC为0.86,AUPRC为0.97.
结论:
- 拟议的基于CNN的模型在UC中有效地区分了MES 0和MES 1.
- 整合结肠和直肠图像特征可以提高诊断准确度.
- 这种人工智能方法显示了改善UC内镜评估的巨大潜力.
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