用人工智能检测食道静脉和评估出血风险:系统性审查
Rahul Ramakrishnan1, Kevin Kuang1, Vijay Rajput1
1Department of Medical Education, Nova Southeastern University Dr. Kiran C. Patel College of Allopathic Medicine (NSU MD), Fort Lauderdale, Florida, USA.
iGIE : innovation, investigation and insights
|February 6, 2026
概括
人工智能 (AI) 在检测食道静脉 (EV) 和预测出血风险方面表现有前途,平均检测准确率为78.67%. 需要对更大的数据集进行进一步的研究,以增强人工智能.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 胃肠病学和肝病学
- 机器学习在临床诊断中的应用
背景情况:
- 食道静脉 (EVs) 是肝硬化和门性高血压的严重并发症.
- 目前通过内镜 (EGD) 进行诊断可能具有观察者间的变异性.
- 人工智能 (AI) 显示出潜力,但需要对EV检测和出血风险进行评估.
研究的目的:
- 系统地审查和评估人工智能算法在检测食道变节方面的能力.
- 评估AI在预测与食道静脉相关的出血风险方面的准确性.
- 在这个领域分析各种AI系统和机器学习算法的性能.
主要方法:
- 在遵守PRISMA指南的基础上进行系统审查.
- 在PubMed,EMBASE和MEDLINE数据库 (1956-2023) 中进行了搜索.
- 从16项研究中提取了AI工具性能,EV检测和出血风险预测准确度的数据.
主要成果:
- 包括16项涉及19个AI系统和26170名患者的研究.
- 人工智能实现了平均78.67%的EV检测准确率和83.2%的水出血预测准确率.
- 绝对增强机器学习显示出出血预测的准确率为100%;放射性模型在EV检测方面达到49%.
结论:
- 人工智能为食道静脉预测和出血风险评估提供了一个潜在的可访问工具.
- 局限性包括数据集依赖性和地理变异的潜在过度解释.
- 未来的人工智能算法需要更大的,经过验证的数据集,以提高临床预测价值.
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