人工智能辅助的葡萄糖皮质激素治疗反应预测活跃性活跃患者的反应
Ning Zhang1, Yuxi Huang2, Bo Peng3,4,5
1Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
一种深度学习模型准确地预测了性结肠炎 (UC) 患者使用病理学和临床数据的葡萄糖皮质醇反应,帮助个性化治疗策略.
科学领域:
- 胃肠病学 胃肠病学
- 数字病理学数字病理学
- 人工智能在医学中的应用
背景情况:
- 葡萄糖皮质类药物是性结肠炎 (UC) 诱导和缓解的标准治疗方法.
- 预测患者对葡萄糖皮质类药物的早期反应对于有效的UC管理至关重要.
- 目前的方法在预测活性UC的早期葡萄糖皮质激素反应方面缺乏准确性.
研究的目的:
- 开发和验证一种深度学习模型,用于预测活跃UC患者的葡萄糖皮质醇反应.
- 评估模型的性能与临床因素对比.
- 确定与治疗反应相关的关键组织病理特征.
主要方法:
- 收集了2个中心212名UC患者的485张整片图像 (WSI) 和临床数据.
- 开发了UCG-SwinT深度学习模型,集成WSIs和临床数据.
- 使用曲线下的面积 (AUC) 和特征可视化Grad-CAM评估模型性能.
主要成果:
- 在结合WSIs和临床数据时,UCG-SwinT模型实现了0.723的AUC (外部测试).
- 综合模型的表现优于仅使用WSIs和所有测试的临床因素的模型.
- 格拉德-CAM确定了炎症细胞增加和微血管扩张作为葡萄糖皮质激素反应的指标.
结论:
- UCG-SwinT深度学习模型显示,在活跃的UC中预测葡萄糖皮质类药物反应具有显著的潜力.
- 这种预测能力可以指导UC患者个性化治疗决策.
- 该模型提供了一种新的方法,以提高UC管理中的精准医学.
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