Machine Learning Approaches for Neurodegenerative Diseases Diagnosis

Dear Colleagues,

Neurodegenerative diseases, such as Parkinson’s disease, Alzheimer’s disease and other types of dementia, Huntington’s disease, motor neuron disease and Prion disease, are progressive brain disorders with great prevalence in the general population. It is estimated that around 10 million people live with Parkinson’s disease worldwide (Marras C., et al. 2018), and a recent report of 2018 (Prince M., et al. 2015) recorded that 33 million people suffer from Alzheimer’s disease. These disorders afflict both the individual and the caregivers, and have a great impact on the global health economy (Xu J., et al. 2017).

Several diagnostic procedures are performed for differential diagnoses, including brain imaging, EEG analysis, molecular analysis, and cognitive, psychological and physical examination (Tzimourta K.D., et al. 2019). The research focuses on novel machine learning and artificial intelligence approaches, to elucidate the pathogenesis of neurodegenerative disorders and provide early diagnosis, aiming to develop effective treatments, improve the quality of the patient’s life and increase life expectancy (Myszczynska M.A., et al. 2020).

The current Special Issue addresses the urgent need for novel strategies and advanced machine learning methods, to provide the foundation for beyond state-of-the-art diagnostic procedures for neurodegenerative diseases.

Dr. Markos G. Tsipouras
Dr. Alexandros T. Tzallas
Prof. Dr. Nikolaos Giannakeas
Dr. Katerina D. Tzimourta
Guest Editors

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