用深度学习自动评估肠炎活动度，提升病理诊断一致性。
Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology
- 基于变压器模型分析肠镜组织全片图像，自动分类炎症活动等级。
- 多类分类的F1分数达0.695，曲线下面积为0.871，表现稳定可靠。
- 注意力图有助于解释模型决策，适合临床辅助诊断场景。
利用标准化组织病理评分系统评估炎症性肠病（IBD）活动度面临资源不足和观察者间差异的挑战。本研究构建深度学习模型，对来自达特茅斯-希奇科克医疗中心2018至2019年间的636名患者、共2,077张40倍放大（0.25微米/像素）的HE染色全切片图像（WSIs）进行活动度分类。由胃肠病学认证病理学家划分为四类：静止、轻度活跃、中度活跃和重度活跃。采用基于变压器的模型，并通过五折交叉验证进行评估。结合HoVerNet分析各活动等级下的中性粒细胞分布。模型预测的注意力图由胃肠病理专家定性评估，显示具备可解释性潜力。模型在四分类任务中的加权平均指标为：曲线下面积（AUC）0.871（95% CI: 0.860–0.883），精确率0.695（95% CI: 0.674–0.715），召回率0.697（95% CI: 0.678–0.716），F1分数0.695（95% CI: 0.674–0.714）。不同活动等级间中性粒细胞分布存在显著差异。结果表明该模型具备稳健的诊断性能，有望提升IBD活动度评估的一致性与效率。
原文摘要 · Abstract (English)
Grading inflammatory bowel disease (IBD) activity using standardized histopathological scoring systems remains challenging due to resource constraints and inter-observer variability. In this study, we developed a deep learning model to classify activity grades in hematoxylin and eosin-stained whole slide images (WSIs) from patients with IBD, offering a robust approach for general pathologists. We utilized 2,077 WSIs from 636 patients treated at Dartmouth-Hitchcock Medical Center in 2018 and 2019, scanned at 40x magnification (0.25 micron/pixel). Board-certified gastrointestinal pathologists categorized the WSIs into four activity classes: inactive, mildly active, moderately active, and severely active. A transformer-based model was developed and validated using five-fold cross-validation to classify IBD activity. Using HoVerNet, we examined neutrophil distribution across activity grades. Attention maps from our model highlighted areas contributing to its prediction. The model classified IBD activity with weighted averages of 0.871 [95% Confidence Interval (CI): 0.860-0.883] for the area under the curve, 0.695 [95% CI: 0.674-0.715] for precision, 0.697 [95% CI: 0.678-0.716] for recall, and 0.695 [95% CI: 0.674-0.714] for F1-score. Neutrophil distribution was significantly different across activity classes. Qualitative evaluation of attention maps by a gastrointestinal pathologist suggested their potential for improved interpretability. Our model demonstrates robust diagnostic performance and could enhance consistency and efficiency in IBD activity assessment.
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