Artificial Intelligence and its future potential in lung cancer screening - Joy Mathew, Christopher; David, Ashwini Maria; Joy Mathew, Chris Mariya (IfADo - Leibniz Research Centre for Working Environment and Human Factors, Dortmund, 2020)
Artificial intelligence (AI) simulates intelligent behavior as well as critical thinking comparable to a human being and can be used to analyze and interpret complex medical data. The application of AI in imaging diagnostics reduces the burden of radiologists and increases the sensitivity of lung cancer screening so that the morbidity and mortality associated with lung cancer can be decreased. In this article, we have tried to evaluate the role of artificial intelligence in lung cancer screening, as well as the future potential and efficiency of AI in the classification of nodules. The relevant studies between 2010-2020 were selected from the PubMed database after excluding animal studies and were analyzed for the contribution of AI. Techniques such as deep learning and machine learning allow automatic characterization and classification of nodules with high precision and promise an advanced lung cancer screening method in the future. Even though several combination models with high performance have been proposed, an effectively validated model for routine use still needs to be improvised. Combining the performance of artificial intelligence with a radiologist’s expertise offers a successful outcome with higher accuracy. Thus, we can conclude that higher sensitivity, specificity, and accuracy of lung cancer screening and classification of nodules is possible through the integration of artificial intelligence and radiology. The validation of models and further research is to be carried out to determine the feasibility of this integration.
review article
EXCLI Journal;Vol. 19. 2020, pp. 1552-1562
Keywords: Artificial intelligence; Lung neoplasms; Early detection of cancer; Machine learning; Lung cancer screening; Artificial intelligence and lung cancer; Low-dose computed tomography; Artificial intelligence in radiology; Computer-aided diagnosis; Convolutional neural networks (cnn); 610