Huang Jin, Zhu Xinyi, Ma Yueying, Zhang Zhenjie, Zhang Jinrong, Hao Zhou, Wu Luyi, Liu Huirong, Wu Huangan, Bao Chunhui
Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Key Laboratory of Acupuncture and Immunological Effects, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Transl Gastroenterol Hepatol. 2025 Jul 7;10:56. doi: 10.21037/tgh-24-117. eCollection 2025.
Inflammatory bowel disease (IBD) is a complex chronic disease of the gastrointestinal tract. This systematic review aimed at highlighting the latest findings on the use of machine learning (ML) in the IBD subtypes, ulcerative colitis and Crohn's disease (CD), with a view to obtaining a basis for the clinical application of ML to differentiate between these subtypes.
We conducted an extensive search of six major databases, including PubMed, Web of Science, Embase, Cochrane Library, Scopus, and Ovid, for entries made between 1 January 2000 and 28 November 2024. The search was focused on identifying studies that used ML to construct diagnostic models for ulcerative colitis and CD. Quality Assessment of Diagnostic Accuracy Studies was used to assess the risk of bias and concerns about the applicability of these studies. The protocol for this review was registered in PROSPERO (CRD42024543036).
After a rigorous screening and assessment process, 31 papers were found to be suitable for inclusion in the review, with a total sample size of 15,140. Most of the included studies were retrospective (n=27, 87%), with the vast majority of studies (n=20, 65%) published between 2021 and 2023. Random forest (RF) was identified as the most commonly used (n=10, 32%), followed by support vector machines (n=9, 29%), and the majority of the studies were focused on model evaluation metrics of ML.
Our findings indicate that ML holds the potential to enhance diagnostic accuracy in distinguishing between ulcerative colitis and CD, particularly through the utilization of models developed from endoscopic and fecal biomarker data based on deep learning and RF.
炎症性肠病（IBD）是一种复杂的胃肠道慢性疾病。本系统评价旨在突出机器学习（ML）在IBD亚型溃疡性结肠炎和克罗恩病（CD）中的最新研究结果，以期为ML在区分这些亚型的临床应用提供依据。
我们对六个主要数据库进行了广泛检索，包括PubMed、科学网、Embase、考克兰图书馆、Scopus和Ovid，检索时间为2000年1月1日至2024年11月28日。检索重点是识别使用ML构建溃疡性结肠炎和CD诊断模型的研究。采用诊断准确性研究的质量评估来评估这些研究的偏倚风险和适用性问题。本评价方案已在国际前瞻性系统评价注册库（PROSPERO）注册（注册号：CRD42024543036）。
经过严格的筛选和评估过程，发现31篇论文适合纳入本评价，总样本量为15140。纳入的研究大多为回顾性研究（n = 27，87%），绝大多数研究（n = 20，65%）发表于2021年至2023年之间。随机森林（RF）被确定为最常用的方法（n = 10，32%），其次是支持向量机（n = 9，29%），并且大多数研究集中于ML的模型评估指标。
我们的研究结果表明，ML有潜力提高区分溃疡性结肠炎和CD的诊断准确性，特别是通过利用基于深度学习和RF的内镜及粪便生物标志物数据开发的模型。
