ABSTRACT
In the last decade, advances in artificial intelligence technology, especially deep learning, have created new opportunities in medical image analysis. The number of studies in this field is increasing day by day and the performance of artificial intelligence is being improved. The aim of the studies is to develop new imaging biomarkers and to create reliable image analysis tools. It has been shown that early and accurate diagnosis of interstitial lung diseases, determination of severity and prediction of prognosis can be possible by the analysis of high-resolution chest computed tomography images with machine learning method, which is a subset of artificial intelligence. Despite all these promising developments, there are still some challenges to be overcome. One of the most important is the need for large and high-quality datasets to develop high-performance models. For this reason, there is a need for the creation of national data pools and international cooperation. Optimal collection, storage, sharing and management of the obtained digital imaging data should be ensured. In addition, measures should be taken to prevent personal data privacy violations.
Keywords: Deep learning, interstitial lung disease, machine learning, radiomics, artificial intelligence
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