Abstract
Abstract Interstitial Lung Diseases (ILDs) represent a heterogeneous group of several rare diseases that are di cult to predict, diagnose and monitor. There are no predictive biomarkers for ILDs, clinical signs are similar to the ones for other lung diseases, the radiological features are not easy to recognize, and require manual radiologist review. Data-driven support for ILD prediction, diagnosis and disease-course monitoring are great unmet need. Numerous image processing techniques and computer-aided diagnostic and decision-making support methods have been developed over the recent years. The current review focuses on such solutions, discussing advancements on the fields of Quantitative CT, Complex Networks, and Convolutional Neural Networks.
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