Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images

Tao Yan; Pak Kin Wong; I Cheong Choi; Chi Man Vong; Hon Ho Yu

doi:10.1016/j.compbiomed.2020.104026

Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images

Comput Biol Med. 2020 Nov:126:104026. doi: 10.1016/j.compbiomed.2020.104026. Epub 2020 Oct 12.

Authors

Tao Yan¹, Pak Kin Wong², I Cheong Choi³, Chi Man Vong⁴, Hon Ho Yu⁵

Affiliations

¹ School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang, 441053, China; Department of Electromechanical Engineering, University of Macau, Taipa, 999078, Macau, China.
² Department of Electromechanical Engineering, University of Macau, Taipa, 999078, Macau, China. Electronic address: fstpkw@um.edu.mo.
³ Kiang Wu Hospital, Macau, China. Electronic address: alexchoi0@gmail.com.
⁴ Department of Computer and Information Science, University of Macau, Taipa, 999078, Macau, China.
⁵ Kiang Wu Hospital, Macau, China.

PMID: 33059237
DOI: 10.1016/j.compbiomed.2020.104026

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.

Keywords: Convolutional neural network; Gastric intestinal metaplasia; Intelligent diagnosis; Magnifying narrow-band imaging; Narrow-band imaging.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Metaplasia / diagnostic imaging
Narrow Band Imaging
Neural Networks, Computer
Pilot Projects
Precancerous Conditions* / diagnostic imaging
Prospective Studies
Retrospective Studies
Stomach Neoplasms* / diagnostic imaging