Author, Institution: Lucas Salvador Bernardo, Kaunas University of Technology
Science area, field of science: Technological Sciences, Informatics Engineering, T007
Scientific Advisor: Prof. Dr. Evaldas Vaičiukynas (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)
Dissertation Defense Board of Informatics Engineering Science Field:
Prof. Dr. Arnas Kačeniauskas (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007) – chairperson
Prof. Dr. Hab. Piotr Artiemjew (University of Warmia and Mazury in Olsztyn, Poland, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Diana Kalibaitienė (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Simona Ramanauskaitė (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Aušra Saudargienė (Vytautas Magnus University, Natural Sciences, Informatics, N009)
Dissertation defense meeting will be at Rectorate Hall of Kaunas University of Technology (K. Donelaičio 73–402, Kaunas)
The doctoral dissertation is available at the library of Kaunas University of Technology (Gedimino g. 50, Kaunas)
Annotation: Parkinson’s disease (PD) is the most common movement disorder, affecting 2-3% of the global population over the age of 65. It ranks as the second most prevalent neurodegenerative disorder after Alzheimer’s disease. The hallmark motor symptoms of PD arise from the degeneration of dopaminergic neurons in the Substantia Nigra region of the brain. Despite advances in technology and healthcare, there is no definitive test for diagnosing PD, nor a cure. However, early diagnosis can significantly improve patients’ quality of life by enabling treatments that slow disease progression and reduce neuronal loss. As a progressive neurological condition, PD often manifests subtle early-stage symptoms, which are frequently overlooked or misdiagnosed as other conditions, such as essential tremor. The integration of AI technologies, particularly Convolutional Neural Networks and Deep Learning, offers a promising solution by analyzing complex datasets of biomarkers associated with Parkinson’s disease, thereby minimizing observational errors. In this context, the present work focuses on developing comprehensive automated methods to detect various PD symptoms. These methods encompass diverse approaches, from analyzing hand-drawn spirals to assessing key-typing pressure, with the primary aim of accelerating the diagnostic process and improving accuracy.
October 25 d. 10:00
Rectorate Hall at Kaunas University of Technology (K. Donelaičio 73-402, Kaunas)
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