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Brain age prediction

Brain Age Prediction (In collaboration with Prof. Tammy Riklin Raviv, The School of Electrical and Computer Engineering, BGU)

The brain undergoes significant structural changes as we age. Brain age prediction based on brain Magnetic Resonance Imaging (MRI) using Deep Learning (DL) has become prominent for exploring healthy brain aging. However, it remains unclear how the brain changes with age and among genders, and how these changes correlate with neurological degeneration. Neurological degeneration is a severe health concern, with Alzheimer’s Disease (AD) being the most prevalent type of dementia. Our cutting-edge research continues the efforts of incorporating artificial intelligence (AI) into medical domains, leveraging DL algorithms for analyzing large-scale medical datasets. The power of AI lies in its ability to analyze large-scale medical datasets, potentially uncovering hidden patterns and associations that may go beyond current knowledge. Understanding how cognitive health varies among different populations can inform the development of targeted interventions and healthcare programs tailored to specific needs. By examining the progression of neurological degeneration, our research could provide critical information for early detection and intervention efforts. Ultimately, this research has the potential to impact healthcare practices, leading to earlier AD diagnosis and treatment initiation, potentially slowing down disease progression, and improving outcomes for affected individuals, making a tangible difference in healthcare.



Gidon Levakov, Alon Kaplan, Anat Yaskolka Meir, Ehud Rinott, Gal Tsaban, Hila Zelicha, Matthias Blüher, Uta Ceglarek, Michael Stumvoll, Ilan Shelef, Galia Avidan, Iris Shai

Ofek Finkelstein, Gidon Levakov, Alon Kaplan, Hila Zelicha, Anat Yaskolka Meir, Ehud Rinott, Gal Tsaban, Anja Veronica Witte, Matthias Blüher, Michael Stumvoll, Ilan Shelef, Iris Shai, Tammy Riklin Raviv, Galia Avidan

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