[2E1-GS-10] AI application

[2E1-GS-10-05] Artificial Intelligence models on chest X-ray images to detect COVID-19 infection

〇Goro Fujiki^1,2,3, Satoshi Kodera², Shinnosuke Sawano², Susumu Katsushika², Hiroki Shinohara², Akifumi Matsuki³, Mai Tanaka³, Isao Goto³, Masaaki Hoshiga¹ (1. Department of Cardiology, Osaka Medical and Pharmaceutical University, 2. Department of Cardiovascular Medicine, The University of Tokyo Hospital, 3. Hirakata City Hospital)

Keywords:COVID-19, chest X-ray, convolutional neural network, transformer, transfer learning

Objective: The purpose of this study was to create artificial intelligence (AI) models for detecting COVID-19 infection on chest X-ray images and to evaluate the performance of the AI models.
Methods: In this study, we used chest X-ray images taken at our institution, and PCR results were used as correct labels. We trained models using convolutional neural network (CNN) and Transformer models and evaluated the performance of the models. We also created transfer learning models using publicly available datasets.
Results: There were 214 COVID-19 positive and 153 COVID-19 negative cases. The ages ranged from 15 to 98 years old (mean 66.0 years old), and there were 208 males and 159 females. The accuracy was 60.8% and the area under the curve was 0.664 with the CNN model using transfer learning. There was no significant difference in the performance of the AI models between CNN and Transformer, and transfer learning did not significantly improve the performance of the AI models.
Conclusion: The performance of the AI models using CNN and Transformer for detecting COVID-19 infection on chest X-ray images taken at our institution was not satisfactory, even with the use of transfer learning.