Artificial Neural Networks In Early Diagnosis Of Neurological Disorders: A Review Of Models, Biomarkers, And Clinical Integration

Abstract

Neurological disorders, ranging from Alzheimer’s disease and Parkinson’s disease to multiple sclerosis and epilepsy, pose significant global health challenges due to their progressive nature and delayed diagnosis. Early detection is critical for effective intervention and management. Traditional diagnostic techniques, although advanced, often lack the sensitivity and scalability required for early-stage recognition. In recent years, Artificial Neural Networks (ANNs), a branch of artificial intelligence inspired by the human brain, have shown immense promise in enhancing the early diagnosis of these disorders. This review synthesizes current advancements in ANN-based models for early detection of neurological disorders and explores their integration with clinical data and neurobiomarkers such as EEG signals, MRI scans, and genetic data. We first discuss the foundational architecture and learning mechanisms of ANNs that make them suitable for handling complex biomedical data. Following this, the paper examines various ANN architectures—including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)—and their applications in diagnosing disorders such as Alzheimer’s, Parkinson’s, epilepsy, and autism spectrum disorders. The paper also highlights the emerging role of hybrid models combining ANN with fuzzy logic, support vector machines, and ensemble learning to improve diagnostic accuracy. A significant focus is placed on biomarkers, including neuroimaging, cerebrospinal fluid analysis, and electrophysiological indicators, and how ANN models are trained to identify diagnostic patterns within them. We explore real-world clinical trials, datasets, and ANN-integrated diagnostic systems currently in use or under development. The challenges of interpretability, data heterogeneity, ethical considerations, and real-time clinical integration are critically assessed. Finally, the review presents future directions emphasizing the need for explainable AI, longitudinal data utilization, and patient-specific modeling. By integrating deep learning with clinical neuroscience, ANN-based systems can revolutionize the landscape of neurological diagnosis. This review underscores their transformative potential while acknowledging the hurdles that must be overcome to achieve seamless clinical adoption.