Babu P Mohan1, Shahab R Khan2, Lena L Kassab3, Suresh Ponnada4, Saurabh Chandan5, Tauseef Ali6, Parambir S Dulai7, Douglas G Adler1, Gursimran S Kochhar8. 1. Gastroenterology & Hepatology, University of Utah, Salt Lake City, Utah, USA. 2. Gastroenterology, Rush University Medical Center, Chicago, Illinois, USA. 3. Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA. 4. Internal Medicine, Roanoke Medical Center, Roanoke, Virginia, USA. 5. Gastroenterology and Hepatology, University of Nebraska Medical Center, Omaha, Nebraska, USA. 6. Gastroenterology, University of Oklahoma/Saint Anthony Hospital, Oklahoma City, Oklahoma, USA. 7. Gastroenterology and Hepatology, University of California, San Diego, California, USA. 8. Division of Gastroenterology and Hepatology, Allegheny Health Network, Pittsburgh, Pennsylvania, USA.
Abstract
BACKGROUND AND AIMS: Diagnosis of GI ulcers and/or hemorrhage by wireless capsule endoscopy (WCE) is limited by the physician-dependent, tedious, time-consuming process of image and/ or video classification. Computer-aided diagnosis (CAD) by convolutional neural network (CNN)-based machine learning may help reduce this burden. Our aim was to conduct a meta-analysis and appraise the reported data. METHODS: Multiple databases were searched (from inception to November 2019), and studies that reported on the performance of CNN in the diagnosis of GI ulcerations and/or hemorrhage on WCE were selected. A random-effects model was used to calculate the pooled rates. In cases where multiple 2 × 2 contingency tables were provided for different thresholds, we assumed the data tables were independent from each other. Heterogeneity was assessed by I2% and 95% prediction intervals. RESULTS: Nine studies were included in our final analysis that evaluated the performance of CNN-based CAD of GI ulcers and/or hemorrhage by WCE. The pooled accuracy was 95.4% (95% confidence interval [CI], 94.3-96.3), sensitivity was 95.5% (95% CI, 94-96.5), specificity was 95.8% (95% CI, 94.7-96.6), positive predictive value was 95.8% (95% CI, 90.5-98.2), and negative predictive value was 96.8% (95% CI, 94.9-98.1). I2% heterogeneity was negligible except for the pooled positive predictive value. CONCLUSIONS: Based on our meta-analysis, CNN-based CAD of GI ulcerations and/or hemorrhage on WCE achieves a high-level performance. The quality of the evidence is robust, and therefore CNN-based CAD has the potential to become the first choice of machine learning to optimize WCE image/video reading.
BACKGROUND AND AIMS: Diagnosis of GI ulcers and/or hemorrhage by wireless capsule endoscopy (WCE) is limited by the physician-dependent, tedious, time-consuming process of image and/ or video classification. Computer-aided diagnosis (CAD) by convolutional neural network (CNN)-based machine learning may help reduce this burden. Our aim was to conduct a meta-analysis and appraise the reported data. METHODS: Multiple databases were searched (from inception to November 2019), and studies that reported on the performance of CNN in the diagnosis of GI ulcerations and/or hemorrhage on WCE were selected. A random-effects model was used to calculate the pooled rates. In cases where multiple 2 × 2 contingency tables were provided for different thresholds, we assumed the data tables were independent from each other. Heterogeneity was assessed by I2% and 95% prediction intervals. RESULTS: Nine studies were included in our final analysis that evaluated the performance of CNN-based CAD of GI ulcers and/or hemorrhage by WCE. The pooled accuracy was 95.4% (95% confidence interval [CI], 94.3-96.3), sensitivity was 95.5% (95% CI, 94-96.5), specificity was 95.8% (95% CI, 94.7-96.6), positive predictive value was 95.8% (95% CI, 90.5-98.2), and negative predictive value was 96.8% (95% CI, 94.9-98.1). I2% heterogeneity was negligible except for the pooled positive predictive value. CONCLUSIONS: Based on our meta-analysis, CNN-based CAD of GI ulcerations and/or hemorrhage on WCE achieves a high-level performance. The quality of the evidence is robust, and therefore CNN-based CAD has the potential to become the first choice of machine learning to optimize WCE image/video reading.
Authors: Babu P Mohan; Antonio Facciorusso; Shahab R Khan; Deepak Madhu; Lena L Kassab; Suresh Ponnada; Saurabh Chandan; Stefano F Crino; Gursimran S Kochhar; Douglas G Adler; Michael B Wallace Journal: Endosc Ultrasound Date: 2022 May-Jun Impact factor: 5.275