Seminar "Predicting Aphasia Type and Severity Using Machine Learning"

On June 19, a scientific seminar on the topic “Predicting Aphasia Type and Severity Using Machine Learning” was held at the Laboratory of Artificial Intelligence for Cognitive Sciences

The speaker was Matvey Kairov (bachelor's student at the HSE) under the supervision of Shalileh Soroosh (Ph.D. in Computer Science, Laboratory Head).

Annotation:
Aphasia is a language disorder that can result from brain damage, often caused by a stroke or traumatic brain injury. The type and severity of aphasia can vary widely among individuals making accurate diagnosis and treatment challenging in real life conditions. In this paper, we propose an approach to determine the type and severity of aphasia using machine and deep learning on brain MRI (Magnetic Resonance Imaging) scans. By leveraging the power of deep neural networks to augment and analyze brain imaging data, we aim to develop a reliable and automated method for classifying different types of aphasia and assessing their severity. Our study will involve training multiple machine and deep learning models to generate synthetic data in order to augment the existing dataset and accurately classify and quantify the characteristics of aphasia. The results of this research have the potential to improve the diagnosis and management of aphasia, leading to better outcomes for individuals affected by this debilitating condition.

Materials:  Presentation from the seminar