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使巴雷特食道的大规模查能够使用在组织病理学中的弱监督深度学习

Kenza Bouzid¹, Harshita Sharma¹, Sarah Killcoyne²

¹Microsoft Health Futures, Cambridge, UK.

Nature communications

|March 12, 2024

概括

一种新的深度学习方法可以从H&E染色的幻灯片中检测Barrett的食道,从而改善早期诊断. 这种人工智能方法有助于病理学家,减少工作量并加强对这种前恶性食道疾病的查.

科学领域:

在瘤学瘤学.
病理学病理学病理学
人工智能的人工智能

背景情况:

巴雷特食道是食道腺癌的前恶性疾病.
目前的诊断方法,如细胞-TFF3测试是资源密集的.
大规模查受限于需要病理学家对多个幻灯片和生物标志物的评估.

研究的目的:

开发一种深度学习方法,仅使用H&E染色幻灯片检测巴雷特食道.
消除在诊断工作流程中需要昂贵的本地化专家注释.
提高大规模查风险人群的能力.

主要方法:

通过使用临床试验数据的诊断标签来训练一个深度学习模型.
该模型在两个独立的数据集上进行了训练和验证,共计1866名患者.
该方法使用常规染色的H&E幻灯片,绕过了TFF3免疫组织化学的需要.

主要成果:

该H&E深度学习模型在发现数据集上实现了91.4%的AUROC,在外部测试数据集上达到87.3%.
性能与TFF3生物标志物模型可比.
半自动化工作流减少了48%的病理学家工作量,而不会影响诊断的准确性.

结论:

在H&E幻灯片上的深度学习为巴雷特食道检测提供了一种可行的,不那么资源密集的方法.
这种人工智能驱动的方法可以显著提高查能力,并协助病理学家.
这些发现支持将AI整合到临床工作流程中,以改善食道癌症预防中的患者结果.

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Barrett's esophagus is a medical condition where the esophageal mucosa is significantly damaged by stomach acid or other digestive fluids, often due to long-term exposure associated with gastroesophageal reflux disease (GERD). In GERD, a weakened or abnormally relaxed lower esophageal sphincter allows stomach acid to flow persistently into the esophagus.
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