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

10.2196/36170 ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. e36170
Author(s):  
Chang Seok Bang ◽  
Jae Jun Lee ◽  
Gwang Ho Baik
Keyword(s):  
Systematic Review ◽  
Diagnostic Test ◽  
Capsule Endoscopy ◽  
Meta Analysis ◽  
Wireless Capsule Endoscopy ◽  
Test Accuracy ◽  
Gastrointestinal Ulcer ◽  
Computer Aided ◽  
Wireless Capsule ◽  
Aided Diagnosis

Computer-aided diagnosis of gastrointestinal protruded lesions using wireless capsule endoscopy: a systematic review and diagnostic test accuracy meta-analysis (Preprint)

2021 ◽  
Author(s):  
Chang Seok Bang ◽  
Jae Jun Lee ◽  
Gwang Ho Baik
Keyword(s):  
Systematic Review ◽  
Capsule Endoscopy ◽  
Diagnostic Performance ◽  
Meta Analysis ◽  
Wireless Capsule Endoscopy ◽  
Test Accuracy ◽  
Computer Aided ◽  
Cad Models ◽  
Wireless Capsule ◽  
Aided Diagnosis

BACKGROUND Wireless capsule endoscopy allows the identification of small intestinal protruded lesions, such as polyps, tumors, or venous structures. However, reading wireless capsule endoscopy images or movies is time-consuming, and minute lesions are easy to miss. Computer-aided diagnosis (CAD) has been applied to improve the efficacy of the reading process of wireless capsule endoscopy images or movies. However, there are no studies that systematically determine the performance of CAD models in diagnosing gastrointestinal protruded lesions. OBJECTIVE This aim of this study was to evaluate the diagnostic performance of CAD models for gastrointestinal protruded lesions using wireless capsule endoscopic images. METHODS Core databases were searched for studies based on CAD models for the diagnosis of gastrointestinal protruded lesions using wireless capsule endoscopy, and data on diagnostic performance were presented. A systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS Twelve studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of protruded lesions were 0.95 (95% confidence interval, 0.93–0.97), 0.89 (0.84–0.92), 0.91 (0.86–0.94), and 74 (43–126), respectively. Subgroup analyses showed robust results. Meta-regression found no source of heterogeneity. Publication bias was not detected. CONCLUSIONS CAD models showed a high performance for the optical diagnosis of gastrointestinal protruded lesions based on wireless capsule endoscopy. CLINICALTRIAL Prospero registration number: id 276623

scholarly journals Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis

2021 ◽  
Author(s):  
Kaiwen Qin ◽  
Jianmin Li ◽  
Yuxin Fang ◽  
Yuyuan Xu ◽  
Jiahao Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Capsule Endoscopy ◽  
Meta Analysis ◽  
Wireless Capsule Endoscopy ◽  
Convolution Neural Network ◽  
Research Progress ◽  
Gi Bleeding ◽  
Wireless Capsule

Abstract Background Wireless capsule endoscopy (WCE) is considered to be a powerful instrument for the diagnosis of intestine diseases. Convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist the detection of WCE images. We aimed to perform a systematic review of the current research progress to the CNN application in WCE. Methods A search in PubMed, SinoMed, and Web of Science was conducted to collect all original publications about CNN implementation in WCE. Assessment of the risk of bias was performed by Quality Assessment of Diagnostic Accuracy Studies-2 risk list. Pooled sensitivity and specificity were calculated by an exact binominal rendition of the bivariate mixed-effects regression model. I2 was used for the evaluation of heterogeneity. Results 16 articles with 23 independent studies were included. CNN application to WCE was divided into detection on erosion/ulcer, gastrointestinal bleeding (GI bleeding), and polyps/cancer. The pooled sensitivity of CNN for erosion/ulcer is 0.96 [95% CI 0.91, 0.98], for GI bleeding is 0.97 (95% CI 0.93–0.99), and for polyps/cancer is 0.97 (95% CI 0.82–0.99). The corresponding specificity of CNN for erosion/ulcer is 0.97 (95% CI 0.93–0.99), for GI bleeding is 1.00 (95% CI 0.99–1.00), and for polyps/cancer is 0.98 (95% CI 0.92–0.99). Conclusion Based on our meta-analysis, CNN-dependent diagnosis of erosion/ulcer, GI bleeding, and polyps/cancer approached a high-level performance because of its high sensitivity and specificity. Therefore, future perspective, CNN has the potential to become an important assistant for the diagnosis of WCE.

Computer-aided diagnosis of gastrointestinal ulcer and hemorrhage using wireless capsule endoscopy: a systematic review and diagnostic test accuracy meta-analysis (Preprint)

2021 ◽  
Author(s):  
Chang Seok Bang ◽  
Jae Jun Lee ◽  
Gwang Ho Baik
Keyword(s):  
Diagnostic Test ◽  
Capsule Endoscopy ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Diagnostic Odds Ratio ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Cad Models ◽  
Sensitivity Specificity ◽  
Wireless Capsule

BACKGROUND 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. OBJECTIVE To evaluate the diagnostic test accuracy of CAD models for gastrointestinal ulcers or hemorrhage using wireless capsule endoscopic images. METHODS 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. RESULTS 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. CONCLUSIONS CAD models showed high performance for the optical diagnosis of gastrointestinal ulcer and hemorrhage in wireless capsule endoscopy. CLINICALTRIAL International Prospective Register of Systematic Reviews (PROSPERO): CRD42021253454 ; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=42021253454.

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