Rubin David T, Kubassova Olga, Weber Christopher R, Adsul Shashi, Freire Marcelo, Biedermann Luc, Koelzer Viktor H, Bressler Brian, Xiong Wei, Niess Jan H, Matter Matthias S, Kopylov Uri, Barshack Iris, Mayer Chen, Magro Fernando, Carneiro Fatima, Maharshak Nitsan, Greenberg Ariel, Hart Simon, Dehmeshki Jamshid, Peyrin-Biroulet Laurent
Department of Pathology, University of Chicago, Chicago, IL, USA.
Image Analysis Group, London, UK.
Inflamm Bowel Dis. 2024 Sep 16. doi: 10.1093/ibd/izae204.
Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by increased stool frequency, rectal bleeding, and urgency. To streamline the quantitative assessment of histopathology using the Nancy Index in UC patients, we developed a novel artificial intelligence (AI) tool based on deep learning and tested it in a proof-of-concept trial. In this study, we report the performance of a modified version of the AI tool.
Nine sites from 6 countries were included. Patients were aged ≥18 years and had UC. Slides were prepared with hematoxylin and eosin staining. A total of 791 images were divided into 2 groups: 630 for training the tool and 161 for testing vs expert histopathologist assessment. The refined AI histology tool utilized a 4-neural network structure to characterize images into a series of cell and tissue type combinations and locations, and then 1 classifier module assigned a Nancy Index score.
In comparison with the proof-of-concept tool, each feature demonstrated an improvement in accuracy. Confusion matrix analysis demonstrated an 80% correlation between predicted and true labels for Nancy scores of 0 or 4; a 96% correlation for a true score of 0 being predicted as 0 or 1; and a 100% correlation for a true score of 2 being predicted as 2 or 3. The Nancy metric (which evaluated Nancy Index prediction) was 74.9% compared with 72.3% for the proof-of-concept model.
We have developed a modified AI histology tool in UC that correlates highly with histopathologists' assessments and suggests promising potential for its clinical application.
溃疡性结肠炎（UC）是一种慢性炎症性肠病，其特征为排便次数增加、直肠出血和便急。为了简化使用南希指数对UC患者组织病理学进行定量评估的过程，我们基于深度学习开发了一种新型人工智能（AI）工具，并在一项概念验证试验中对其进行了测试。在本研究中，我们报告了该AI工具改良版本的性能。
纳入了来自6个国家的9个研究点。患者年龄≥18岁且患有UC。制备苏木精和伊红染色的玻片。总共791张图像被分为两组：630张用于训练该工具，161张用于与专家组织病理学家的评估进行对比测试。改良后的AI组织学工具利用4神经网络结构将图像特征化为一系列细胞和组织类型组合及位置，然后由1个分类器模块给出南希指数评分。
与概念验证工具相比，各项特征的准确性均有所提高。混淆矩阵分析显示，南希评分为0或4时，预测标签与真实标签的相关性为80%；真实评分为0被预测为0或1时，相关性为96%；真实评分为2被预测为2或3时，相关性为100%。南希指标（评估南希指数预测）为74.9%，而概念验证模型为72.3%。
我们在UC中开发了一种改良的AI组织学工具，其与组织病理学家的评估高度相关，并显示出良好的临床应用潜力。
