Neuroimaging Techniques for Monitoring and Diagnosing Amyotrophic Lateral Sclerosis: Current Applications of AI and Emerging Innovations

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by degeneration of upper and lower motor neurons, leading to muscle weakness, paralysis, and ultimately respiratory failure. Motor neurons are specialized nerve cells that control voluntary muscle movement and are located in the motor cortex of the brain, the brainstem, and the spinal cord. As these neurons deteriorate, the brain progressively loses its ability to communicate with skeletal muscles, producing the hallmark motor symptoms of ALS. Although the biological mechanisms underlying ALS are not yet fully understood, research has implicated several contributing processes, including mitochondrial dysfunction, glutamate excitotoxicity, oxidative stress, and neuroinflammation.

Neuroimaging techniques such as magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), and positron emission tomography (PET) have increasingly been used to identify structural, functional, and metabolic changes associated with ALS, often preceding overt clinical decline. This report summarizes recent developments in the application of artificial intelligence (AI) and machine learning (ML) methods to ALS neuroimaging. Rather than presenting an exhaustive review, we focus on representative studies that illustrate how computational models are applied to neuroimaging data for disease classification, biomarker identification, and assessment of disease progression. Relevant literature was identified through targeted searches of PubMed.

Across imaging modalities, AI-based approaches have demonstrated the ability to detect ALS-related patterns that may be difficult to identify using conventional visual or statistical analyses. Studies most commonly employ support vector machines, random forests, and deep learning architectures applied to MRI and DTI features, with reported improvements in classification performance. However, many studies remain limited by small sample sizes, diverse imaging protocols, limited availability of control data, and challenges related to model interpretability and clinical validation.

Overall, AI-assisted analysis of neuroimaging data represents a rapidly evolving area of ALS research. Continued progress will depend on improved data sharing, standardized imaging pipelines, and validation across diverse patient cohorts before these approaches can be reliably integrated into clinical practice.