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

ABSTRACT: 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 detection of a wide range of diseases and is generally performed as a part of regular medical checkups. However, when it comes to findings that can be classified as within the normal range but are not considered disease-related, the thresholds of physicians' findings can vary to some extent, therefore it is necessary to define a new evaluation method and quantify it. The implementation of such methods is difficult and expensive in terms of time and labor. In this study, a total of 83,005 chest X

SUBMITTER: Nakagawa S 

PROVIDER: S-EPMC10942047 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature