Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images. (November 2020)

• Record Type: Journal Article
• Authors: Yan, Tao; Wong, Pak Kin; Choi, I. Cheong; Vong, Chi Man; Yu, Hon Ho
• Is Part Of: Computers in biology and medicine. Volume 126(2020)
• Publication Date: 2020-11
• Subjects: Gastric intestinal metaplasia -- Intelligent diagnosis -- Narrow-band imaging -- Magnifying narrow-band imaging -- Convolutional neural network; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.104026
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Gastric intestinal metaplasia (GIM) is a precancerous lesion of gastric cancer. Currently, diagnosis of GIM is based on the experience of a physician, which is liable to interobserver variability. Thus, an intelligent diagnostic (ID) system, based on narrow-band and magnifying narrow-band images, was constructed to provide objective assistance in the diagnosis of GIM. Method: We retrospectively collected 1880 endoscopic images (1048 GIM and 832 non-GIM) via biopsy from 336 patients confirmed histologically as GIM or non-GIM, from the Kiang Wu Hospital, Macau. We developed an ID system with these images using a modified convolutional neural network algorithm. A separate test dataset containing 477 pathologically confirmed images (242 GIM and 235 non-GIM) from 80 patients was used to test the performance of the ID system. Experienced endoscopists also examined the same test dataset, for comparison with the ID system. One of the challenges faced in this study was that it was difficult to obtain a large number of training images. Thus, data augmentation and transfer learning were applied together. Results: The area under the receiver operating characteristic curve was 0.928 for the pre-patient analysis of the ID system, while the sensitivities, specificities, and accuracies of the ID system against those of the human experts were (91.9% vs. 86.5%, p-value = 1.000) (86.0% vs. 81.4%, p-value = 0.754), and (88.8% vs. 83.8%, p-value = 0.424), respectively. Even though the three indices of the ID system were slightly higher than those of the human experts, there were no significant differences. Conclusions: In this pilot study, a novel ID system was developed to diagnose GIM. This system exhibits promising diagnostic performance. It is believed that the proposed system has the potential for clinical application in the future. Highlights: A new intelligent system is proposed to diagnose gastric intestinal metaplasia. The system is based on narrow-band imaging (NBI) and magnifying NBI images. Transfer learning and data augmentation are used since the data is limited. System evaluation is comprehensive, including comparison with human experts. Diagnostic accuracy is satisfactory, and the diagnosis is fast.