Clinical Implementation of Deep Learning in ThoracicRadiology: Potential Applications and Challenges
- Author:
Eui Jin HWANG
1
;
Chang Min PARK
Author Information
- Publication Type:Review Article
- From:Korean Journal of Radiology 2020;21(5):511-525
- CountryRepublic of Korea
- Language:English
- Abstract: Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under activeinvestigation with deep learning technology, which has shown promising performance in various tasks, including detection,classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential forclinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facingseveral challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, andincomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deeplearning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.
