基于人工智能的内镜超声诊断胃肠道间质瘤的准确性：一项荟萃分析。
Diagnostic accuracy of endoscopic ultrasound with artificial intelligence for gastrointestinal stromal tumors: A meta-analysis.
机构信息
Department of Gastroenterology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang Province, China.
Department of Gastroenterology, Digestive Endoscopy Center, Changhai Hospital, Naval Medical University, Shanghai, China.
出版信息
J Dig Dis. 2022 May;23(5-6):253-261. doi: 10.1111/1751-2980.13110.
OBJECTIVES
Gastrointestinal stromal tumors (GISTs) are thought to have a malignant potential. However, utilizing endoscopic ultrasound (EUS) to differentiate GISTs from other types of subepithelial lesions (SELs) remains challenging. Artificial intelligence (AI)-based diagnostic systems for EUS have been reported to have a promising performance, although the results of the previous studies remain controversial. In this meta-analysis, we aimed to assess the diagnostic accuracy of AI-based EUS in distinguishing GISTs from other SELs.
METHODS
A literature search was conducted on MEDLINE and EMBASE databases to identify relevant articles. The sensitivity, specificity, and area under the summary receiver operating characteristic curve (AUROC) of eligible studies were analyzed.
RESULTS
Seven studies were eligible for the final analysis. The combined sensitivity and specificity of AI-based EUS were 0.93 (95% confidence interval [CI] 0.88-0.96) and 0.78 (95% CI 0.67-0.87), respectively. The overall diagnostic odds ratio of AI-based EUS for GISTs was 36.74 (95% CI 17.69-76.30) with an AUROC of 0.94.
CONCLUSIONS
AI-based EUS showed high diagnostic ability and might help better differentiate GISTs from other SELs. More prospective studies on the diagnosis of GISTs using AI-based EUS are warranted in clinical setting.
目的
胃肠道间质瘤（GIST）被认为具有恶性潜能。然而，利用内镜超声（EUS）来区分 GIST 与其他类型的黏膜下病变（SELs）仍然具有挑战性。基于人工智能（AI）的 EUS 诊断系统已被报道具有有前景的性能，尽管之前的研究结果仍存在争议。在这项荟萃分析中，我们旨在评估基于 AI 的 EUS 在区分 GIST 与其他 SELs 方面的诊断准确性。
方法
对 MEDLINE 和 EMBASE 数据库进行文献检索，以确定相关文章。分析了纳入研究的敏感性、特异性和汇总受试者工作特征曲线下面积（AUROC）。
结果
有 7 项研究符合最终分析的条件。基于 AI 的 EUS 的联合敏感性和特异性分别为 0.93（95%置信区间 [CI] 0.88-0.96）和 0.78（95% CI 0.67-0.87）。基于 AI 的 EUS 对 GIST 的总体诊断优势比为 36.74（95% CI 17.69-76.30），AUROC 为 0.94。
结论
基于 AI 的 EUS 显示出较高的诊断能力，可能有助于更好地区分 GIST 与其他 SELs。在临床环境中，需要更多使用基于 AI 的 EUS 诊断 GIST 的前瞻性研究。
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