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  1. Home
  3. Machine Learning And Deep Learning For Neurological Disease Analysis: A Systematic Review Across Five Major Disorders.
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  3. Machine Learning And Deep Learning For Neurological Disease Analysis: A Systematic Review Across Five Major Disorders.

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Machine Learning and Deep Learning for Neurological Disease Analysis: A Systematic Review Across Five Major

Kazi Nur Uddin1, Partho Ghose2, Ebrima Njie1

  • 1Department of Mathematical Sciences, Kent State University, Kent, OH, 44242, United States.

Neuroscience
|June 6, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) advances neurological disease research using machine learning (ML) and deep learning (DL). This review synthesizes recent AI applications in neuroimaging and clinical data for conditions like Alzheimer's and stroke, highlighting progress and persistent challenges.

Keywords:
Alzheimer’s disease (AD)Brain tumor (BT)Deep learning (DL)Hybrid and multimodal frameworksMachine learning (ML)Neurological disease

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Artificial Intelligence (AI) is increasingly vital in neurological disease research, driven by extensive neuroimaging, clinical, and sensor data.
  • A synthesis of recent machine learning (ML) and deep learning (DL) applications in this field is currently limited.
  • This review addresses the need for a comprehensive overview of AI's role in neurological disease research.

Purpose of the Study:

  • To systematically review and synthesize recent advancements in ML and DL for five major neurological conditions.
  • To analyze AI methodologies applied to neuroimaging, clinical, and sensor data.
  • To identify emerging AI paradigms and persistent challenges in clinical translation.

Main Methods:

  • Systematic literature review following PRISMA 2020 guidelines.
  • Searched PubMed, Scopus, and Web of Science for studies from January 2021 to March 2026.
  • Included 206 eligible articles on Alzheimer's disease, stroke, Parkinson's disease, brain tumors, and traumatic brain injury (TBI).

    • Main Results:

    • Convolutional and encoder-decoder architectures are prevalent in imaging tasks.
    • Hybrid and multimodal approaches increasingly integrate imaging with clinical and sensor data.
    • Key advances include transformer models for Alzheimer's diagnosis, real-time stroke detection, improved Parkinson's detection, brain tumor classification, and TBI outcome prediction.

    Conclusions:

    • Emerging AI paradigms like federated learning and foundation models address data limitations and privacy concerns.
    • Significant progress has been made in AI-driven diagnosis and outcome prediction for various neurological diseases.
    • Challenges in generalizability, interpretability, and clinical translation necessitate further development of robust AI systems.