Advancing Rare Neurological Disorder Diagnosis: Addressing Challenges with Systematic Reviews and AI-Driven MRI Meta-Trans Learning Framework for NeuroDegenerative Disorders.
Authors
Affiliations (2)
Affiliations (2)
- Department of Computer Science and Information Technology, Central University of Jammu, Samba, India, 181143. Electronic address: [email protected].
- Department of Computer Science and Information Technology, Central University of Jammu, Samba, India, 181143.
Abstract
Neurological Disorders (ND) affect a large portion of the global population, impacting the brain, spinal cord, and nerves. These disorders fall into categories such as NeuroDevelopmental (NDD), NeuroBiological (NBD), and NeuroDegenerative (ND<sub>e</sub>) disorders, which range from common to rare conditions. While Artificial Intelligence (AI) has advanced healthcare diagnostics, training Machine Learning (ML) and Deep Learning (DL) models for early detection of rare neurological disorders remains a challenge due to limited patient data. This data scarcity poses a significant public health issue. Meta_Trans Learning (M<sub>TA</sub>L), which integrates Meta-Learning (M<sub>t</sub>L) and Transfer Learning (TL), offers a promising solution by leveraging small datasets to extract expert patterns, generalize findings, and reduce AI bias in healthcare. This research systematically reviews studies from 2018 to 2024 to explore how ML and M<sub>TA</sub>L techniques are applied in diagnosing NDD, NBD, and ND<sub>e</sub> disorders. It also provides statistical and parametric analysis of ML and DL methods for neurological disorder diagnosis. Lastly, the study introduces a MRI-based ND<sub>e</sub>-M<sub>TA</sub>L framework to aid healthcare professionals in early detection of rare neuro disorders, aiming to enhance diagnostic accuracy and advance healthcare practices.