在内镜中使用人工智能预测Helicobacter pylori感染:系统性审查和元分析
Yiwen Jiang1, Hengxu Yan2, Jiatong Cui1
1The First Clinical College, China Medical University, Shenyang, China.
Helicobacter
|March 21, 2025
概括
与临床医生相比,内镜中的人工智能 (AI) 在检测Helicobacter pylori感染方面表现出更高的准确性. 由于研究异质性,需要进一步的研究.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 杆菌 (H. pylori) 感染是一个重大的全球健康问题.
- 准确及时诊断H. pylori对于有效的治疗和管理至关重要.
- 内镜是诊断H. pylori的主要工具,但其准确性可能有所不同.
研究的目的:
- 评估人工智能 (AI) 系统在使用内镜图像检测H. pylori感染方面的诊断性能.
- 将AI的诊断精度与初级和高级临床医生的诊断精度进行比较.
主要方法:
- 在主要数据库 (PubMed,Embase,Web of Science) 进行了系统的文献搜索,直到2025年1月.
- 在16项涉及25002名参与者的研究中,使用双变随机效应模型进行了元分析.
- 计算和比较了包括聚合灵敏度,特异性和AUC在内的诊断准确度指标.
主要成果:
- 人工智能显示出高的诊断性能,内部验证的综合灵敏度和特异性为0.91和0.91,外部验证的0.91和0.94.
- 人工智能显著优于初级临床医生 (灵敏度0.76,特异性0.75) 和高级临床医生 (灵敏度0.81,特异性0.89).
- AI的曲线下的面积 (AUC) 为0.96 (内部) 和0.98 (外部),而初级临床医生为0.81和高级临床医生为0.92.
结论:
- 与人类内镜医生相比,基于内镜的AI在检测H. pylori方面表现出更高的诊断准确性.
- 研究中的高度异质性需要谨慎的解释和进一步的验证.
- 未来的研究应该专注于外部验证,以确认AI在H. pylori诊断中的临床实用性.
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