Chapter 17 - New trends in deep learning for neuroimaging analysis and disease prediction

Abstract

In the last few decades, deep learning techniques for diagnosing and predicting disease conditions from neuroimaging have attracted much attention and interest from the scientific community. Big data and artificial intelligence approaches and innovations are currently being utilized to generate large datasets from images, text, sounds, graphs, and signals. New trends in the utilization of deep learning for disease prediction in neurology, oncology, cardiology, and other areas entail converting patient electronic health records, biological system information, physiological signals, biomarkers, and biomedical images to cognitive functions. The current trends in deep learning techniques focus on utilizing neuroimaging analysis to evaluate alterations in local morphological topographies of different brain sub-regions and then predict novel disorder-linked brain patterns. Hence, this chapter presents a detailed overview of different approaches in deep learning for the prediction of major brain diseases such as mild cognitive impairment, Alzheimer's disease, brain tumors, depressive disorders, traumatic brain injury, schizophrenia, Parkinson's disease, autism spectrum disease, attention-deficit hyperactivity disorder, epilepsy, stroke, multiple sclerosis, and more. The chapter also discusses the current challenges of utilizing deep learning in assessing brain disorders in neuroimaging data.