Journal article
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
Annals of gastroenterology, v 34(1), pp 20-25
01 Jan 2021
PMID: 33414617
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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 2x2 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%.
Metrics
Details
- Title
- 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
- Creators
- Babu P. Mohan - University of UtahShahab R. Khan - Rush UniversityLena L. Kassab - College Station Medical CenterSuresh Ponnada - College Station Medical CenterNabeeha Mohy-Ud-Din - Allegheny Hlth Network, Gastroenterol & Hepatol, Pittsburgh, PA 15212 USASaurabh Chandan - College Station Medical CenterParambir S. Dulai - Univ Calif San Diego, Gastroenterol & Hepatol, San Diego, CA 92103 USAGursimran S. Kochhar - Allegheny Hlth Network, Gastroenterol & Hepatol, Pittsburgh, PA 15212 USA
- Publication Details
- Annals of gastroenterology, v 34(1), pp 20-25
- Publisher
- Hellenic Soc Gastroenterology
- Number of pages
- 10
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- General Internal Medicine
- Web of Science ID
- WOS:000605685300003
- Scopus ID
- 2-s2.0-85099657657
- Other Identifier
- 991022076698704721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Gastroenterology & Hepatology