ARTIFICIAL INTELLIGENCE–BASED DETERMINATION OF CEREBRAL HEMISPHERE MORPHOMETRIC PARAMETERS USING MAGNETIC RESONANCE IMAGING (MRI)

Abstract

Quantitative morphometric analysis of cerebral hemispheres using magnetic resonance imaging (MRI) has become an essential component of modern neuroradiology. Structural parameters such as hemisphere volume, cortical thickness, and surface area serve as sensitive biomarkers for neurodegenerative, neurovascular, and neurodevelopmental disorders. However, traditional manual and semi-automated segmentation approaches are time-consuming and prone to inter-observer variability. Recent advances in artificial intelligence (AI), especially deep learning-based segmentation models, have demonstrated high accuracy in structural brain analysis. This study provides a comprehensive AI-based framework for automated cerebral hemisphere morphometry and describes its implementation via the NeuroMorph AI web platform. The proposed system combines deep learning segmentation, volumetric computing, cortical surface reconstruction, and structural digital reporting by analyzing MRI images, providing a scalable and clinically applicable solution that is compatible with the modern healthcare digital transformation.