Dhali Arkadeep, Kipkorir Vincent, Maity Rick, Srichawla Bahadar S, Biswas Jyotirmoy, Rathna Roger B, Bharadwaj Hareesha Rishab, Ongidi Ibsen, Chaudhry Talha, Morara Gisore, Waithaka Maryann, Rugut Clinton, Lemashon Miheso, Cheruiyot Isaac, Ojuka Daniel, Ray Sukanta, Dhali Gopal Krishna
Academic Unit of Gastroenterology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
School of Medicine and Population Health, University of Sheffield, Sheffield, UK.
J Gastroenterol Hepatol. 2025 May;40(5):1105-1118. doi: 10.1111/jgh.16931. Epub 2025 Mar 13.
Capsule endoscopy (CE) is a valuable tool used in the diagnosis of small intestinal lesions. The study aims to systematically review the literature and provide a meta-analysis of the diagnostic accuracy, specificity, sensitivity, and negative and positive predictive values of AI-assisted CE in the diagnosis of small bowel lesions in comparison to CE.
Literature searches were performed through PubMed, SCOPUS, and EMBASE to identify studies eligible for inclusion. All publications up to 24 November 2024 were included. Original articles (including observational studies and randomized control trials), systematic reviews, meta-analyses, and case series reporting outcomes on AI-assisted CE in the diagnosis of small bowel lesions were included. The extracted data were pooled, and a meta-analysis was performed for the appropriate variables, considering the clinical and methodological heterogeneity among the included studies. Comprehensive Meta-Analysis v4.0 (Biostat Inc.) was used for the analysis of the data.
A total of 14 studies were included in the present study. The mean age of participants across the studies was 54.3 years (SD 17.7), with 55.4% men and 44.6% women. The pooled accuracy for conventional CE was 0.966 (95% CI: 0.925-0.988), whereas for AI-assisted CE, it was 0.9185 (95% CI: 0.9138-0.9233). Conventional CE exhibited a pooled sensitivity of 0.860 (95% CI: 0.786-0.934) compared with AI-assisted CE at 0.9239 (95% CI: 0.8648-0.9870). The positive predictive value for conventional CE was 0.982 (95% CI: 0.976-0.987), whereas AI-assisted CE had a PPV of 0.8928 (95% CI: 0.7554-0.999). The pooled specificity for conventional CE was 0.998 (95% CI: 0.996-0.999) compared with 0.5367 (95% CI: 0.5244-0.5492) for AI-assisted CE. Negative predictive values were higher in AI-assisted CE at 0.9425 (95% CI: 0.9389-0.9462) versus 0.760 (95% CI: 0.577-0.943) for conventional CE.
AI-assisted CE displays superior diagnostic accuracy, sensitivity, and positive predictive values albeit the lower pooled specificity in comparison with conventional CE. Its use would ensure accurate detection of small bowel lesions and further enhance their management.
胶囊内镜（CE）是诊断小肠病变的一种重要工具。本研究旨在系统回顾文献，并对人工智能辅助胶囊内镜（AI-assisted CE）与传统胶囊内镜相比在诊断小肠病变中的诊断准确性、特异性、敏感性以及阴性和阳性预测值进行荟萃分析。
通过PubMed、SCOPUS和EMBASE进行文献检索，以确定符合纳入标准的研究。纳入截至2024年11月24日的所有出版物。纳入的文献包括原始文章（包括观察性研究和随机对照试验）、系统评价、荟萃分析以及报告人工智能辅助胶囊内镜诊断小肠病变结果的病例系列。对提取的数据进行汇总，并针对纳入研究间的临床和方法学异质性，对适当变量进行荟萃分析。使用Comprehensive Meta-Analysis v4.0（Biostat公司）进行数据分析。
本研究共纳入14项研究。各研究参与者的平均年龄为54.3岁（标准差17.7），男性占55.4%，女性占44.6%。传统胶囊内镜的合并准确率为0.966（95%置信区间：0.925 - 0.988），而人工智能辅助胶囊内镜的合并准确率为0.9185（95%置信区间：0.9138 - 0.9233）。传统胶囊内镜的合并敏感性为0.860（95%置信区间：0.786 - 0.934），而人工智能辅助胶囊内镜的敏感性为0.9239（95%置信区间：0.8648 - 0.9870）。传统胶囊内镜的阳性预测值为0.982（95%置信区间：0.976 - 0.987），而人工智能辅助胶囊内镜的阳性预测值为0.8928（95%置信区间：0.7554 - 0.999）。传统胶囊内镜的合并特异性为0.998（95%置信区间：0.996 - 0.999），而人工智能辅助胶囊内镜的特异性为0.5367（95%置信区间：0.5244 - 0.5492）。人工智能辅助胶囊内镜的阴性预测值更高，为0.9425（95%置信区间：0.9389 - 0.9462），而传统胶囊内镜的阴性预测值为0.760（95%置信区间：0.577 - 0.943）。
与传统胶囊内镜相比，人工智能辅助胶囊内镜虽然合并特异性较低，但显示出更高的诊断准确性、敏感性和阳性预测值。其应用将确保准确检测小肠病变并进一步改善对这些病变的管理。
