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Personalized Treatment of Respiratory Diseases Using Artificial Intelligence and Interoperability with e-Health Systems
Myška, Vojtěch ; Drotár,, Peter (referee) ; Brezany, Peter (referee) ; Burget, Radim (advisor)
Corticosteroid (CS) treatment in patients with Long COVID aims to prevent the progression from active post-inflammatory changes to fibrosis scarring. However, CS have side effects, which may sometimes be severe. Some patients might not require any treatment as their post-inflammatory changes resolve spontaneously. This dissertation thesis aims to develop an artificial intelligence (AI) based approach that allows personalized treatment of patients with Long COVID and a design of modular architecture allowing seamless interoperability of AI models with the information systems used in healthcare facilities. The first part of the thesis deals with the foundation of the state-of-the-art of using AI algorithms to recommend CS treatment in patients with Long COVID, who have the risk of permanent lung damage. This study examines how various parameters from different examinations influence the accuracy of the AI models. The most effective model achieves an accuracy of 73.68 %, a balanced accuracy of 73.52 %, and an AUC of 0.7469. These results prove that a trained AI model on a correctly chosen set of parameters from various medical examinations is effective and can be used as a decision-support tool for further treatment courses. The second part focuses on developing a modular architecture that allows interoperability between AI models and the information system of health facilities. Its specific implementation for early COVID-19 detection, incorporating DeepCovidXR models, is presented. In the performance test, the average processing time of X-ray images is 11.53 seconds using the CPU and 2.78 seconds with the GPU. Both values meet the maximum permissible analysis time set at 20 seconds. The results presented in both sections have been implemented and are currently used at the Olomouc University Hospital.
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New approaches to determination pathophysiological changes in patients with cystic fibrosis
Doušová, Tereza ; Dřevínek, Pavel (advisor) ; Kreslová, Marcela (referee) ; Gayillyová, Renata (referee)
New approaches to determination pathophysiological changes in patients with cystic fibrosis Cystic fibrosis (CF) is a life-limiting disease caused by mutation in the cystic fibrosis transmembrane regulator (CFTR) gene. To date, more than 2,000 mutations in the CFTR gene have been described, of which only 360 are directly related to CF. In a group of patients carrying mutations of unknown or variable clinical significance, it may be difficult not only to diagnose CF but also to facilitate clinical studies to determine the efficacy of new low - molecular weight compounds targeting disrupted CFTR protein. These so-called CFTR modulators have opened a new era in causal treatment of CF. To maximize the effect of these new therapies, not only the patient's genotype, but also the individual rate of response is crucial. In recent years, intestinal organoids have been used as an ex vivo model to determine the degree of CFTR function and at the same time to predict the therapeutic response to available therapeutic molecules. In our project, using the patient's native tissue and cultures of intestinal organoids derived from this tissue, we demonstrated varying degrees of CFTR residual function in a total of 14 patients with CF (0-39.7% of healthy control function). We characterized de novo mutation of the CFTR gene in...
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