机器学习在性结肠炎和克罗恩病的差异诊断:一个系统的审查
Jin Huang1, Xinyi Zhu1,2, Yueying Ma1
1Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
机器学习 (ML) 在区分炎症性肠病 (IBD) 亚型,性结肠炎和克罗恩病 (CD) 方面表现有前途. 使用内镜和便生物标记数据的模型,特别是深度学习和随机森林,可以提高诊断准确性.
科学领域:
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 炎症性肠病 (IBD) 包括性结肠炎和克罗恩病 (CD),复杂的慢性胃肠道疾病.
- 准确区分IBD亚型对于有效的临床管理和治疗策略至关重要.
研究的目的:
- 系统地审查关于机器学习 (ML) 应用的最新发现,以区分性结肠炎和克罗恩病.
- 评估ML在IBD亚型诊断中的临床应用潜力.
主要方法:
- 从2000年到2024年,在六个主要数据库 (PubMed,科学网,Embase,Cochrane图书馆,Scopus,Ovid) 进行了广泛的系统文献搜索.
- 包括使用ML用于性结肠炎和CD诊断模型的研究,并使用QUADAS评估偏差风险.
- 审查协议已在PROSPERO (CRD42024543036) 中注册.
主要成果:
- 31项涉及15140名参与者的研究符合纳入标准.
- 大多数研究 (87%) 是回顾性研究,其中很大一部分 (65%) 发表于2021-2023.
- 随机森林 (32%) 和支持矢量机 (29%) 是最常用的ML算法,重点是模型评估指标.
结论:
- 机器学习表明,在区分性结肠炎和克罗恩病的诊断准确性方面,有很大的潜力.
- 利用内镜和便生物标记数据的模型,特别是使用深度学习和随机森林算法的模型,显示出临床实施的希望.
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