AI methods tailored for improving diagnostic accuracy and characterization of neurological disorders

Background: Clinical datasets used in deep learning applications often suffer from limited sample sizes. However, advancements in AI techniques including transfer learning, generative adversarial networks have paved the way to overcome, at least partially, this limitation.

Purpose: Our primary goal is to design, develop, and employ dedicated AI methods that enhance the training and generalization capabilities of deep learning models when working with small-sized datasets in the context of various neurological disorders. By leveraging these specialized AI techniques, we aim to improve the accuracy and precision of diagnostic processes, enabling a more comprehensive characterization of neurological conditions.