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

10.2196/29682 ◽  
2021 ◽  
Author(s):  
Chang Seok Bang ◽  
Jae Jun Lee ◽  
Gwang Ho Baik
Keyword(s):  
Systematic Review ◽  
Diagnostic Test ◽  
Meta Analysis ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Endoscopic Images ◽  
Computer Aided ◽  
Aided Diagnosis

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

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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