Revealing Hidden Neuroanatomical Patterns of the Brain for Diagnosis and Personalized Therapy Using Artificial Intelligence and Neuroimaging

Abstract

The integration of artificial intelligence (AI) with advanced neuroimaging techniques represents one of the most important developments in contemporary neuroscience and clinical neuroanatomy. The aim of this narrative review is to summarize how machine learning and deep learning methods may be applied to neuroimaging data in order to reveal hidden anatomical and functional patterns of the brain with direct clinical relevance. Neuroanatomy plays a central role in this process, providing the structural framework for interpreting functional, metabolic and electrophysiological signals.

AI-based analysis enables the detection of subtle alterations in gray and white matter, identification of disrupted brain networks, and integration of multimodal imaging data beyond the limits of human perception and conventional statistical approaches. These capabilities contribute to earlier and more accurate diagnosis, improved disease differentiation, identification of neurobiological subtypes, and more reliable prognostic assessment. In addition to diagnostic applications, AI supports the development of personalized therapeutic strategies, including targeted neuromodulation, neuroimaging-guided surgery, adaptive brain–computer interfaces, and individualized neurofeedback.

Despite existing challenges related to data heterogeneity, model interpretability, and ethical regulation, the synergy between AI and neuroimaging represents a critical step toward precision medicine. By leveraging detailed neuroanatomical information, AI-driven approaches enable therapies tailored to the unique brain structure and network organization of each individual.