人工智能在食管癌内镜诊断中的作用:系统回顾和荟萃分析。

Nadia Guidozzi, Nainika Menon, Swathikan Chidambaram, Sheraz Rehan Markar
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引用次数: 0

摘要

早期发现食管癌是有限的内镜准确诊断细微的宏观病变。内窥镜解释受专业知识、诊断技能和人为错误的影响。内窥镜领域的人工智能(AI)正日益弥合这一差距。本系统综述和荟萃分析巩固了人工智能在食管癌内镜诊断中的应用证据。采用Pubmed、MEDLINE和Ovid EMBASE数据库进行系统评价,纳入人工智能在食管癌内镜诊断中的作用。还进行了荟萃分析。14项研究(1590例患者)评估了AI在食管鳞状细胞癌内镜诊断中的应用,其敏感性和特异性分别为91.2%(84.3-95.2%)和80%(64.3-89.9%)。9项研究(478例患者)评估了人工智能诊断食管腺癌的能力,其总敏感性和特异性分别为93.1%(86.8-96.4)和86.9%(81.7-90.7)。其余的研究形成了定性总结。人工智能技术作为内窥镜检查的辅助手段,可以帮助准确、早期地发现食管恶性肿瘤。在识别早期癌症和评估肿瘤侵袭深度方面,它比内窥镜医生表现出更好的结果,而且不需要专门的技能。尽管有很好的结果,但在实时内窥镜检查中的应用是有限的,需要进一步的多中心试验来准确评估其在常规实践中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis.

Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.

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