Chang Seok Bang1, Jae Jun Lee2, Gwang Ho Baik3. 1. Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea; Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, Korea; Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea; Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Korea. 2. Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea; Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Korea; Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon, Korea. 3. Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea; Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, Korea.
Abstract
BACKGROUND AND AIMS: Diagnosis of esophageal cancer or precursor lesions by endoscopic imaging depends on endoscopist expertise and is inevitably subject to interobserver variability. Studies on computer-aided diagnosis (CAD) using deep learning or machine learning are on the increase. However, studies with small sample sizes are limited by inadequate statistical strength. Here, we used a meta-analysis to evaluate the diagnostic test accuracy (DTA) of CAD algorithms of esophageal cancers or neoplasms using endoscopic images. METHODS: Core databases were searched for studies based on endoscopic imaging using CAD algorithms for the diagnosis of esophageal cancer or neoplasms and presenting data on diagnostic performance, and a systematic review and DTA meta-analysis were performed. RESULTS: Overall, 21 and 19 studies were included in the systematic review and DTA meta-analysis, respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD algorithms for the diagnosis of esophageal cancer or neoplasms in the image-based analysis were 0.97 (95% confidence interval [CI], 0.95-0.99), 0.94 (95% CI, 0.89-0.96), 0.88 (95% CI, 0.76-0.94), and 108 (95% CI, 43-273), respectively. Meta-regression showed no heterogeneity, and no publication bias was detected. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD algorithms for the diagnosis of esophageal cancer invasion depth were 0.96 (95% CI, 0.86-0.99), 0.90 (95% CI, 0.88-0.92), 0.88 (95% CI, 0.83-0.91), and 138 (95% CI, 12-1569), respectively. CONCLUSIONS: CAD algorithms showed high accuracy for the automatic endoscopic diagnosis of esophageal cancer and neoplasms. The limitation of a lack in performance in external validation and clinical applications should be overcome.
BACKGROUND AND AIMS: Diagnosis of esophageal cancer or precursor lesions by endoscopic imaging depends on endoscopist expertise and is inevitably subject to interobserver variability. Studies on computer-aided diagnosis (CAD) using deep learning or machine learning are on the increase. However, studies with small sample sizes are limited by inadequate statistical strength. Here, we used a meta-analysis to evaluate the diagnostic test accuracy (DTA) of CAD algorithms of esophageal cancers or neoplasms using endoscopic images. METHODS: Core databases were searched for studies based on endoscopic imaging using CAD algorithms for the diagnosis of esophageal cancer or neoplasms and presenting data on diagnostic performance, and a systematic review and DTA meta-analysis were performed. RESULTS: Overall, 21 and 19 studies were included in the systematic review and DTA meta-analysis, respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD algorithms for the diagnosis of esophageal cancer or neoplasms in the image-based analysis were 0.97 (95% confidence interval [CI], 0.95-0.99), 0.94 (95% CI, 0.89-0.96), 0.88 (95% CI, 0.76-0.94), and 108 (95% CI, 43-273), respectively. Meta-regression showed no heterogeneity, and no publication bias was detected. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD algorithms for the diagnosis of esophageal cancer invasion depth were 0.96 (95% CI, 0.86-0.99), 0.90 (95% CI, 0.88-0.92), 0.88 (95% CI, 0.83-0.91), and 138 (95% CI, 12-1569), respectively. CONCLUSIONS: CAD algorithms showed high accuracy for the automatic endoscopic diagnosis of esophageal cancer and neoplasms. The limitation of a lack in performance in external validation and clinical applications should be overcome.
Authors: Jin Lin Tan; Mohamed Asif Chinnaratha; Richard Woodman; Rory Martin; Hsiang-Ting Chen; Gustavo Carneiro; Rajvinder Singh Journal: Front Med (Lausanne) Date: 2022-06-22
Authors: Eun Jeong Gong; Chang Seok Bang; Kyoungwon Jung; Su Jin Kim; Jong Wook Kim; Seung In Seo; Uhmyung Lee; You Bin Maeng; Ye Ji Lee; Jae Ick Lee; Gwang Ho Baik; Jae Jun Lee Journal: J Pers Med Date: 2022-06-27