AMSTERDAM - Today’s conversation starts with the use of deep neural networks (DNNs) in Alzheimer's disease, with Sara Moein explaining how DNNs are used to differentiate between mild and severe Alzheimer's cases through brain imaging. She outlines the steps involved in this process, including data acquisition, preprocessing, feature extraction, and model validation, highlighting the complexity and sophistication of these techniques.

Emir and Sara discuss how machine learning (ML) models enhance the accuracy and speed of Alzheimer's diagnosis compared to traditional methods. ML's ability to analyze large, complex datasets - including medical records, genetic information, and brain imaging - represents a significant advantage over conventional approaches that rely on smaller, simpler datasets. Addressing bias in ML models is another cr