Machine learning and deep learning for neurological disease analysis: A systematic review across five major disorders.
Authors
Affiliations (9)
Affiliations (9)
- Department of Mathematical Sciences, Kent State University, Kent, OH, 44242, United States. Electronic address: [email protected].
- Department of Biological and Agricultural Engineering, Texas A&M University, College Station, 77843, TX, United States. Electronic address: [email protected].
- Department of Mathematical Sciences, Kent State University, Kent, OH, 44242, United States. Electronic address: [email protected].
- Department of Computer Science, Kent State University, Kent, OH, 44242, United States. Electronic address: [email protected].
- Department of Computer Science, Kent State University, Kent, OH, 44242, United States. Electronic address: [email protected].
- Department of Mathematical Sciences, Kent State University, Kent, OH, 44242, United States. Electronic address: [email protected].
- Alliance University, Bengaluru, Karnataka, India; Golden Gate University Worldwide, San Francisco, CA, United States. Electronic address: [email protected].
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, 710048, China. Electronic address: [email protected].
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, 02115, United States; Department of Pharmacology & Toxicology, The University of Arizona, Tucson, AZ, 85721, United States. Electronic address: [email protected].
Abstract
Artificial Intelligence (AI) has become integral to the research of neurological diseases due to the rapid expansion of neuroimaging, clinical, physiological, and wearable data. However, the concise synthesis of recent machine learning (ML) and deep learning (DL) remains limited. This systematic review analyzes studies published between January 2021 and March 2026 on five major conditions- Alzheimer's disease, stroke, Parkinson's disease, brain tumors, and traumatic brain injury (TBI)-following the PRISMA 2020 guidelines and a structured search of PubMed, Scopus, and Web of Science, yielding 206 eligible articles. The results show that convolutional and encoder-decoder architectures dominate imaging tasks, whereas hybrid and multimodal approaches increasingly combine imaging with clinical and sensor data. Emerging paradigms, including federated learning, self-supervised learning, and foundation models, address data scarcity, privacy, and cross-institutional variability. Key advances include high-performing transformer-based models for Alzheimer's diagnosis, real-time stroke detection by CT/MRI, improved Parkinson's detection by multimodal fusion, hybrid models for brain tumor classification, and outcome prediction in TBI. Despite these gains, challenges in generalizability, interpretability, and clinical translation persist, underscoring the need for more robust and clinically reliable AI systems to address these issues.