J Korean Soc Radiol.  2019 Sep;80(5):872-879. 10.3348/jksr.2019.80.5.872.

Application of Artificial Intelligence in Lung Cancer Screening

Affiliations
  • 1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • 2Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea. cmpark.morphius@gmail.com

Abstract

Lung cancer is a leading cause of deaths due to cancer, worldwide. At present, low-dose computed tomography (CT) is the only established screening method for reducing lung cancer mortality. However, several challenges must be overcome, to ensure the implementation of lung cancer screening, which include a large number of expected low-dose CT examinations and relative shortage of experienced radiologists for interpreting them. The use of artificial intelligence has garnered attention in this regard. A deep learning technique, which is a subclass of machine learning methods, involving the learning of data representations in an end-to-end manner, has already demonstrated outstanding performance in medical image analysis. Several studies are exploring the possibility of deep learning-based applications in medical domains, including radiology. In lung cancer screening, computer-aided detection, report generation, prediction of malignancy in the detected nodules, and prognosis prediction can be considered for the application of artificial intelligence. This article will cover the current status of deep learning approaches, their limitations, and their potential in lung cancer screening programs.


MeSH Terms

Artificial Intelligence*
Cause of Death
Learning
Lung Neoplasms*
Lung*
Machine Learning
Mass Screening*
Methods
Mortality
Prognosis
Tomography, X-Ray Computed
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