Deep learning for smartphone-aided detection system of Helicobacter Pylori in gastric biopsy - Guanmeng Gao; Zihan Wei; Fei Pei; Yajie Du; Beiying Liu (Scientific Reports)
Abstract Helicobacter pylori (HP) have chronically infected more than half of the world’s population and is a cause of chronic gastritis, peptic ulcers and gastric carcinoma. The manual detection of HP in a glass slide with a microscope is extremely time-consuming and might miss at least 14% of HP-positive cases due to eye fatigue of pathologists. Here, a total of 270 gastric biopsy specimens were selected. All stained slides were scanned for analysis by the Faster-R-CNN with ResNet 50 or VGG16, then the model performance was evaluated. Furthermore, the real-time microscopic field, smartphone and AI algorithm were connected through 5G networks and the AI results were sent back to the smartphone for confirmation by the pathologists. Finally, the diagnoses of different pathologists with/without AI assistance were compared. As results, we present a deep learning framework (the Faster-R-CNN with ResNet 50) which can automatically detect HP of gastric biopsies and achieve 89.23% accuracy. We found the real-time system can effectively improve the consistency and accuracy of diagnosis among different pathologists in detecting HP because of real-time alert for lesions with sounds and labels. Thus we concluded that our smartphone-aided detection system by deep learning is the first real-time AI-assisted diagnostic tool for Helicobacter pylori screening. It can be used with a traditional microscope, does not interfere the pathologist’s perspective during routine slide diagnosis, and does not add extra steps or observation time for pathologists.