Luo De, Kuang Fei, Du Juan, Zhou Mengjia, Liu Xiangdong, Luo Xinchen, Tang Yong, Li Bo, Su Song
Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Department of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China.
Front Oncol. 2022 Jun 10;12:855175. doi: 10.3389/fonc.2022.855175. eCollection 2022.
The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images.
Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated.
Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95-0.96), 0.95 (95% CI, 0.94-0.95), 10.76 (95% CI, 7.33-15.79), 0.07 (95% CI, 0.04-0.11), and 173.93 (95% CI, 81.79-369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91-0.96), 0.90 (95% CI, 0.88-0.92), 6.14 (95% CI, 2.06-18.30), 0.07 (95% CI, 0.04-0.11), and 69.13 (95% CI, 14.73-324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87-0.88), 0.88 (95% CI, 0.87-0.88), 7.20 (95% CI, 4.32-12.00), 0.14 (95% CI, 0.09-0.23), and 48.77 (95% CI, 24.98-95.19), respectively.
On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC.
https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).
本研究旨在评估人工智能（AI）利用内镜图像检测早期上消化道癌（EUGIC）的诊断能力。
检索数据库中有关利用内镜图像进行AI辅助诊断EUGIC的研究。计算合并曲线下面积（AUC）、灵敏度、特异度、阳性似然比（PLR）、阴性似然比（NLR）以及诊断比值比（DOR），并给出95%置信区间（CI）。
总体而言，34项研究纳入了我们的最终分析。在17项基于图像的研究中，研究早期食管癌（EEC）检测时，合并AUC、灵敏度、特异度、PLR、NLR和DOR分别为0.98、0.95（95%CI，0.95 - 0.96）、0.95（95%CI，0.94 - 0.95）、10.76（95%CI，7.33 - 15.79）、0.07（95%CI，0.04 - 0.11）和173.93（95%CI，81.79 - 369.83）。在7项基于患者的研究中，研究EEC检测时，合并AUC、灵敏度、特异度、PLR、NLR和DOR分别为0.98、0.94（95%CI，0.91 - 0.96）、0.90（95%CI，0.88 - 0.92）、6.14（95%CI，2.06 - 18.30）、0.07（95%CI，0.04 - 0.11）和69.13（95%CI，14.73 - 324.45）。在15项基于图像的研究中，研究早期胃癌（EGC）检测时，合并AUC、灵敏度、特异度
