scholarly journals Computer-aided diagnosis of diminutive colorectal polyps in endoscopic images: A systematic review and meta-analysis of diagnostic test accuracy (Preprint)

2021 ◽  
Author(s):  
Chang Seok Bang ◽  
Jae Jun Lee ◽  
Gwang Ho Baik
Keyword(s):  
Diagnostic Test ◽  
Meta Analysis ◽  
Colorectal Polyps ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Rectosigmoid Colon ◽  
Histologic Diagnosis ◽  
Endoscopic Images ◽  
Cad Models ◽  
Aided Diagnosis

BACKGROUND The majority of colorectal polyps are diminutive and benign, especially for those in the rectosigmoid colon, and resecting these polyps is not cost-effective. Advancements in image-enhanced endoscopy has improved the optical prediction of histology in colorectal polyps. However, subjective interpretability and inter-/intra-observer variability prohibited the widespread implementation. Studies on computer-aided diagnosis (CAD) are increasing; however, their small sample size limits the statistical significance. OBJECTIVE to evaluate the diagnostic test accuracy of CAD models in predicting the histology of diminutive colorectal polyps using endoscopic images. METHODS Core databases were searched for studies based on endoscopic imaging using CAD models for the histologic diagnosis of diminutive colorectal polyps and presenting data on diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS Overall, 13 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of diminutive colorectal polyps (adenomatous or neoplastic vs. nonadenomatous or nonneoplastic) were 0.96 (95% confidence interval, 0.93–0.97), 0.93 (0.91–0.95), 0.87 (0.76–0.93), and 87 (38–201), respectively. Meta-regression showed no heterogeneity, and no publication bias was detected. Subgroup analyses showed robust results. The negative predictive value of CAD models for the diagnosis of adenomatous polyp in the rectosigmoid colon was 0.96 (0.95–0.97), exceeding the threshold of the “diagnosis and leave” strategy. CONCLUSIONS CAD models show potential for the optical histologic diagnosis of diminutive colorectal polyps using endoscopic images.

scholarly journals Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy (Preprint)

2020 ◽  
Author(s):  
Chang Seok Bang ◽  
Jae Jun Lee ◽  
Gwang Ho Baik
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Helicobacter Pylori ◽  
Diagnostic Test ◽  
Meta Analysis ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Endoscopic Images ◽  
Number Of Patients ◽  
H Pylori

BACKGROUND <i>Helicobacter pylori</i> plays a central role in the development of gastric cancer, and prediction of <i>H pylori</i> infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of <i>H pylori</i> infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification. OBJECTIVE This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of <i>H pylori</i> infection using endoscopic images. METHODS Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of <i>H pylori</i> infection and with application of AI for the prediction of <i>H pylori</i> infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of <i>H pylori</i> infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with <i>H pylori</i> infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images. CONCLUSIONS An AI algorithm is a reliable tool for endoscopic diagnosis of <i>H pylori</i> infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome. CLINICALTRIAL PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957

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.

scholarly journals Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy

10.2196/21983 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e21983
Author(s):  
Chang Seok Bang ◽  
Jae Jun Lee ◽  
Gwang Ho Baik
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Helicobacter Pylori ◽  
Diagnostic Test ◽  
Meta Analysis ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Endoscopic Images ◽  
Number Of Patients ◽  
H Pylori

Background Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification. Objective This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images. Methods Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. Results Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images. Conclusions An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome. Trial Registration PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957

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