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Shin K, Lee JS, Lee JY, Lee H, Kim J, Byeon JS, Jung HY, Kim DH, Kim N. An Image Turing Test on Realistic Gastroscopy Images Generated by Using the Progressive Growing of Generative Adversarial Networks. J Digit Imaging 2023; 36:1760-1769. [PMID: 36914855 PMCID: PMC10406771 DOI: 10.1007/s10278-023-00803-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/16/2023] Open
Abstract
Generative adversarial networks (GAN) in medicine are valuable techniques for augmenting unbalanced rare data, anomaly detection, and avoiding patient privacy issues. However, there were limits to generating high-quality endoscopic images with various characteristics, such as peristalsis, viewpoints, light sources, and mucous patterns. This study used the progressive growing of GAN (PGGAN) within the normal distribution dataset to confirm the ability to generate high-quality gastrointestinal images and investigated what barriers PGGAN has to generate endoscopic images. We trained the PGGAN with 107,060 gastroscopy images from 4165 normal patients to generate highly realistic 5122 pixel-sized images. For the evaluation, visual Turing tests were conducted on 100 real and 100 synthetic images to distinguish the authenticity of images by 19 endoscopists. The endoscopists were divided into three groups based on their years of clinical experience for subgroup analysis. The overall accuracy, sensitivity, and specificity of the 19 endoscopist groups were 61.3%, 70.3%, and 52.4%, respectively. The mean accuracy of the three endoscopist groups was 62.4 [Group I], 59.8 [Group II], and 59.1% [Group III], which was not considered a significant difference. There were no statistically significant differences in the location of the stomach. However, the real images with the anatomical landmark pylorus had higher detection sensitivity. The images generated by PGGAN showed highly realistic depictions that were difficult to distinguish, regardless of their expertise as endoscopists. However, it was necessary to establish GANs that could better represent the rugal folds and mucous membrane texture.
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Affiliation(s)
- Keewon Shin
- Biomedical Engineering Research Center, Asan Medical Center, Seoul, Republic of Korea
| | - Jung Su Lee
- Department of Gastroenterology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
- Seoul Samsung Internal Medicine Clinic, Seoul, Republic of Korea
| | - Ji Young Lee
- Department of Health Screening and Promotion Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Hyunsu Lee
- Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jeongseok Kim
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jeong-Sik Byeon
- Department of Gastroenterology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Hwoon-Yong Jung
- Department of Gastroenterology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Do Hoon Kim
- Department of Gastroenterology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
| | - Namkug Kim
- Biomedical Engineering Research Center, Asan Medical Center, Seoul, Republic of Korea.
- Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Medical Center, Seoul, Republic of Korea.
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