| Περίληψη: | Asthma is a common chronic disease of the respiratory system causing significant disability and societal burden. It affects over 334 million people worldwide and generates costs exceeding $USD 56 billion in 2011 in the United States. Managing Asthma involves controlling symptoms, preventing exacerbations, and maintaining lung function. Improving asthma control affects patients' daily life and is associated with a reduced risk of exacerbations and lung function impairment, reducing the cost of asthma care and indirect costs associated with reduced productivity. Understanding the complex dynamics of the pulmonary system and the lung's response to disease, injury, and treatment is fundamental to the advancement of Asthma treatment. Computational models of the respiratory system seek to provide a theoretical framework to understand the interaction between structure and function. Their application can improve pulmonary medicine by a patient-specific approach to medicinal methodologies optimizing the delivery given the personalized geometry and personalized ventilation patterns while introducing a patient-specific technique that maximizes drug delivery. A three-fold objective addressed within this dissertation becomes prominent at this point. The first part refers to the comprehension of pulmonary pathophysiology and the mechanics of Asthma and subsequently of constrictive pulmonary conditions in general. The second part refers to designing and implementing tools that facilitate personalized medicine to improve delivery and effectiveness. Finally, the third part refers to the self-management of the condition, meaning that medical personnel and patients have access to tools and methods that allow the first party to easily track the course of the condition and the second party, i.e. the patient to easily self-manage it alleviating the significant burden from the health system.
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