Patient-specific computational modelling, simulation and real-time processing for constrictive respiratory diseases
Creators: name: Nousias, Stavros
Publisher: name: Zenodo
Year: 2023

description: This presentation describes and outlines the doctoral dissertation entitled "Patient-specific modelling, simulation and real time processing for respiratory diseases" elaborated by Stavros Nousias and defended on 4th of July 2022 at the University of Patras, Patras, Greece.
Asthma is a common chronic disease of the respiratory system causing significant disability and societal burden. It affects over 500 million people worldwide and generates costs exceeding 56 billion per year in the European Union (EU). Managing asthma involves controlling symptoms, preventing exacerbations, and maintaining lung function. Improving asthma control affects the daily life of patients and is associated with a reduced risk of exacerbations and lung function impairment, reduces the cost of asthma care and indirect costs associated with reduced productivity. Despite the widespread availability of therapies in randomized controlled trials, different levels of asthma control have been observed in several studies using well-validated self-assessment questionnaires, such as the Asthma Control Questionnaire (ACQ) and the Asthma Control Test (ACT). 
Traditionally the primary pillar of asthma control is monitoring and intervention. This route entails asynchronous spirometry data, questionnaires, environmental parameters and medication monitoring. However is an optimized asthma control approach, improvements in multiple stages take place, including data management, intelligent data processing, data mining predictive models fused with computational models of the pulmonary system. A primary layer of assessing the concentrated data corresponds to a system-level solution that is forwarded and visualized via innovative interfaces to the medical personnel. A second level includes simplistic data mining and handmade decision trees that generate simple notifications to the doctor and the patient. Complex artificial intelligence models process the data to extract biomarkers and predict exacerbations, hospital visits, and conditions at a third level.
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. Well-designed computational models can make sense of highly complex systems, but they can also become quite complicated. A model will never wholly represent reality, and simplifying assumptions are always required for tractability. Integrative computational models can bring together two or more aspects of lung function to study their combined contribution to overall function. Their application can improve pulmonary medicine by introducing 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.