对于刺激性肠综合征和炎症性肠病的差异诊断的人工智能方法:系统审查
Iolanda Valentina Popa1, Mihaela Dranga2, Otilia Nedelciuc3
1Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania. iolivp@gmail.com.
Journal of gastrointestinal and liver diseases : JGLD
|December 26, 2025
概括
人工智能 (AI) 和机器学习 (ML) 在区分刺激性肠综合征 (IBS) 和炎症性肠病 (IBD) 方面表现有前途. 需要进一步的研究来克服局限性,并将这些先进的诊断工具整合到临床实践中.
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:
- 区分刺激性肠综合征 (IBS) 和炎症性肠病 (IBD) 是具有挑战性的,因为症状重叠和复杂的病理生理学.
- 对于IBS和IBD的传统诊断方法有局限性,需要新的方法.
研究的目的:
- 审查人工智能 (AI) 和机器学习 (ML) 在IBS和IBD差异诊断中的应用.
- 评估AI/ML方法在区分这些胃肠道疾病的准确性和效率.
主要方法:
- 综述了七项使用各种AI/ML技术的近期研究.
- 分析使用临床,遗传,微生物和成像数据进行IBS/IBD差异化的研究.
主要成果:
- 基于AI的模型在区分IBS和IBD方面表现出高度的灵敏度和特异性.
- 逻辑回归,随机森林,神经网络和支持矢量机器等算法表现出了显著的性能.
- 确定的主要生物标志物包括长非编码RNA,DNA甲基化概况和肠道微生物群化合物.
结论:
- AI/ML方法具有显著的潜力,可以改善IBS和IBD的差异诊断.
- 目前的局限性包括小样本大小,数据异质性和概括性问题.
- 建议采用标准化方案和多中心研究来进行临床验证和将其纳入实践.
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