Convolutional neural networks in the computer-aided diagnosis of Helicobacter pylori infection and non-causal comparison to physician endoscopists: a systematic review with meta-analysis

Background Helicobacter pylori (H. pylori) infection, if left untreated, can cause gastric cancer, among other serious morbidities. In recent times, a growing body of evidence has evaluated the use of a type of artificial intelligence (AI) known as "deep learning" in the computer-aided diagnosis of H. pylori using convolutional neural networks (CNN). We conducted this meta-analysis to evaluate the pooled rates of performance of CNN-based AI in the diagnosis of H. pylori infection. Methods Multiple databases were searched (from inception to June 2020) and studies that reported on the performance of CNN in the diagnosis of H. pylori infection 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. Results Five studies were included in our final analysis. Images used were from a combination of white-light, blue laser imaging, and linked color imaging. The pooled accuracy for detecting H. pylori infection with AI was 87.1% (95% confidence interval [CI] 81.8-91.1), sensitivity was 86.3% (95%CI 80.4-90.6), and specificity was 87.1% (95%CI 80.5-91.7). The corresponding performance metrics for physician endoscopists were 82.9% (95%CI 76.7-87.7), 79.6% (95%CI 68.1-87.7), and 83.8% (95%CI 72-91.3), respectively. Based on non-causal subgroup comparison methods, CNN seemed to perform equivalently to physicians. Conclusion Based on our meta-analysis, CNN-based computer-aided diagnosis of H. pylori infection demonstrated an accuracy, sensitivity, and specificity of 87%.