ARTIFICIAL INTELLIGENCE–BASED BIOPHYSICAL MODELING OF NEURAL SIGNAL DYNAMICS FOR EARLY NEUROLOGICAL DISORDER DETECTION

Abstract

Early detection of neurological disorders remains a major clinical challenge due to the complex and dynamic nature of neural signal activity. Biophysical modeling combined with artificial intelligence (AI) provides a promising framework for analyzing electrophysiological patterns and identifying early pathological alterations. The present study investigates the integration of AI-driven algorithms with biophysical modeling of electroencephalographic (EEG) signals to enhance early diagnostic accuracy. A computational–analytical study design was implemented using simulated and clinically referenced neural signal datasets. Biophysical parameters including signal amplitude variability, frequency band power distribution, entropy indices, and nonlinear dynamic coefficients were extracted. Machine learning classifiers were applied to evaluate predictive performance. The results demonstrate that AI-integrated biophysical modeling significantly improves early-stage neurological disorder discrimination compared with conventional signal analysis approaches. Increased sensitivity and predictive accuracy were observed particularly in detecting early epileptiform activity and neurodegenerative pattern deviations. The findings suggest that combining quantitative biophysical frameworks with artificial intelligence enhances diagnostic precision and supports the development of predictive neurodiagnostic platforms. This interdisciplinary approach represents a critical advancement in computational neurobiophysics and precision neurology.