Quantitative evaluation model of variable diagnosis for chest X-ray images using deep learning

• Published in: PLOS digital health Vol. 3; no. 3; p. e0000460
• Main Authors: Nakagawa, Shota, Ono, Naoaki, Hakamata, Yukichika, Ishii, Takashi, Saito, Akira, Yanagimoto, Shintaro, Kanaya, Shigehiko
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.03.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Artificial intelligence; Deep learning; Information sources; Machine learning; Medical screening; Neural networks; Scoliosis; X-rays

The purpose of this study is to demonstrate the use of a deep learning model in quantitatively evaluating clinical findings typically subject to uncertain evaluations by physicians, using binary test results based on routine protocols. A chest X-ray is the most commonly used diagnostic tool for the...