White Ciara D, Chetty Runjan, Weldon John, Morrissey Maria E, Sykes Rob, Gîrleanu Corina, Colleuori Mirko, Fitzgerald Jenny, Power Adam, Ahmad Ajaz, Carmody Seán, Moulin Pierre, O'Shea Donal, Aslam Muhammad, Dada Mahomed A, Loughrey Maurice B, McManus Martine C, Nowak Klaudia M, McCombe Kristopher, Hutton Sinead, Rafferty Máirín, Mulligan Niall
Department of Histopathology, Mater Misericordiae University Hospital, Dublin, Ireland.
Deciphex, DCU Alpha Innovation Campus, Dublin, Ireland.
Histopathology. 2025 Feb;86(3):373-384. doi: 10.1111/his.15331. Epub 2024 Oct 3.
To create and validate a weakly supervised artificial intelligence (AI) model for detection of abnormal colorectal histology, including dysplasia and cancer, and prioritise biopsies according to clinical significance (severity of diagnosis).
Triagnexia Colorectal, a weakly supervised deep learning model, was developed for the classification of colorectal samples from haematoxylin and eosin (H&E)-stained whole slide images. The model was trained on 24 983 digitised images and assessed by multiple pathologists in a simulated digital pathology environment. The AI application was implemented as part of a point and click graphical user interface to streamline decision-making. Pathologists assessed the accuracy of the AI tool, its value, ease of use and integration into the digital pathology workflow.
Validation of the model was conducted on two cohorts: the first, on 100 single-slide cases, achieved micro-average model specificity of 0.984, micro-average model sensitivity of 0.949 and micro-average model F1 score of 0.949 across all classes. A secondary multi-institutional validation cohort, of 101 single-slide cases, achieved micro-average model specificity of 0.978, micro-average model sensitivity of 0.931 and micro-average model F1 score of 0.931 across all classes. Pathologists reflected their positive impressions on the overall accuracy of the AI in detecting colorectal pathology abnormalities.
We have developed a high-performing colorectal biopsy AI triage model that can be integrated into a routine digital pathology workflow to assist pathologists in prioritising cases and identifying cases with dysplasia/cancer versus non-neoplastic biopsies.
创建并验证一种弱监督人工智能（AI）模型，用于检测结直肠组织学异常，包括发育异常和癌症，并根据临床意义（诊断严重程度）对活检样本进行优先级排序。
开发了一种弱监督深度学习模型Triagnexia Colorectal，用于对苏木精和伊红（H&E）染色的全玻片图像中的结直肠样本进行分类。该模型在24983张数字化图像上进行训练，并在模拟数字病理学环境中由多名病理学家进行评估。该AI应用程序作为点击式图形用户界面的一部分实现，以简化决策过程。病理学家评估了AI工具的准确性、价值、易用性以及与数字病理学工作流程的整合情况。
在两个队列上对该模型进行了验证：第一个队列包含100个单玻片病例，所有类别中模型的微平均特异性为0.984，微平均敏感性为0.949，微平均F1分数为0.949。第二个多机构验证队列包含101个单玻片病例，所有类别中模型的微平均特异性为0.978，微平均敏感性为0.931，微平均F1分数为0.931。病理学家对AI在检测结直肠病理异常方面的总体准确性给予了积极评价。
我们开发了一种高性能的结直肠活检AI分诊模型，该模型可集成到常规数字病理学工作流程中，以协助病理学家对病例进行优先级排序，并识别发育异常/癌症病例与非肿瘤性活检病例。
