Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis

• Published in: Journal of medical Internet research Vol. 23; no. 12; p. e33267
• Main Authors: Bang, Chang Seok, Lee, Jae Jun, Baik, Gwang Ho
• Format: Journal Article
• Language: English
• Published: Canada Gunther Eysenbach MD MPH, Associate Professor 14.12.2021; JMIR Publications
• Subjects: Accuracy; Artificial intelligence; Automation; Capsule Endoscopy; Computers; Deep learning; Diagnostic tests; Diagnostic Tests, Routine; Efficacy; Endoscopy; Hemorrhage; Humans; Keywords; Librarians; Libraries; Machine learning; Medical diagnosis; Meta-analysis; Neural networks; Review; Subject heading schemes; Systematic review; Ulcer - diagnostic imaging; Ulcers; Wireless networks; meta-analysis; accuracy; endoscopy; computer-aided diagnosis; machine learning; artificial intelligence; capsule endoscopy; gastrointestinal; prediction models; performance; review; ulcer; diagnostic; hemorrhage
• DOI: 10.2196/33267
• ISSN: 1438-8871; 1439-4456

Interpretation of capsule endoscopy images or movies is operator-dependent and time-consuming. As a result, computer-aided diagnosis (CAD) has been applied to enhance the efficacy and accuracy of the review process. Two previous meta-analyses reported the diagnostic performance of CAD models for gastrointestinal ulcers or hemorrhage in capsule endoscopy. However, insufficient systematic reviews have been conducted, which cannot determine the real diagnostic validity of CAD models. To evaluate the diagnostic test accuracy of CAD models for gastrointestinal ulcers or hemorrhage using wireless capsule endoscopic images. We conducted core databases searching for studies based on CAD models for the diagnosis of ulcers or hemorrhage using capsule endoscopy and presenting data on diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. Overall, 39 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of ulcers (or erosions) were .97 (95% confidence interval, .95-.98), .93 (.89-.95), .92 (.89-.94), and 138 (79-243), respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of hemorrhage (or angioectasia) were .99 (.98-.99), .96 (.94-0.97), .97 (.95-.99), and 888 (343-2303), respectively. Subgroup analyses showed robust results. Meta-regression showed that published year, number of training images, and target disease (ulcers vs erosions, hemorrhage vs angioectasia) was found to be the source of heterogeneity. No publication bias was detected. CAD models showed high performance for the optical diagnosis of gastrointestinal ulcer and hemorrhage in wireless capsule endoscopy.