Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children

The aims of this project are: * to build a large database of clinical, biological and radiological data collected from pediatric patients with severe asthma; * to design and train a forecaster model based on DL techniques to predict asthma severity in children; * to estimate transition probabilities between asthma severity levels using a multi-state Markov model taking into account qualitative and quantitative information obtained from CT imaging. Our evaluation of DL-severity and existing clinical scores in childhood asthma is expected to reveal that emerging methodologies assisted by DL techniques can provide accurate severity predictions, when compared with existing clinical scores. Such an accurate prediction model would allow pediatricians to identify features that are the most indicative of severity and progression of asthma and would be employed to formulate intervention strategies and early medical attention for children.