In this episode, we explore the groundbreaking research by Dr. Kolachalama and his team on addressing the global challenge of dementia, specifically Alzheimer’s disease (AD), which affects nearly 10 million new individuals annually. Their innovative approach integrates artificial intelligence and healthcare, paving the way for enhanced diagnosis of cognitive impairments.

The team's work revolves around a deep learning framework that performs multiple diagnostic steps successively to distinguish between normal cognition, mild cognitive impairment, AD, and non-AD dementias. This model utilizes a wide array of routinely collected clinical data such as demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments.

Interestingly, their models not only compete with the diagnostic accuracy of practicing neurologists and neuroradiologists but also reveal disease-specific patterns of degenerative changes in the brain. They further leverage interpretability methods in computer vision to validate these computational predictions with established medical diagnosis standards.

Join us in this fascinating conversation as we delve into the future of dementia diagnosis and treatment with Dr. Kolachalama.

Keywords: Dr. Kolachalama, Deep Learning, Dementia, Alzheimer’s Disease, Artificial Intelligence, Health Diagnosis, Neurology, Cognitive Impairment, Mild Cognitive Impairment, Non-AD Dementias, Neuropathological Lesions.

Qiu, S., Miller, M.I., Joshi, P.S. et al. Multimodal deep learning for Alzheimer’s disease dementia assessment. Nat Commun 13, 3404 (2022). https://doi.org/10.1038

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