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Related Concept Videos

Brain Imaging01:14

Brain Imaging

228
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
228

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Neuroimage analysis using artificial intelligence approaches: a systematic review.

Eric Jacob Bacon1,2, Dianning He1, N'bognon Angèle D'avilla Achi3

  • 1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.

Medical & Biological Engineering & Computing
|April 25, 2024
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Summary
This summary is machine-generated.

Artificial intelligence (AI) significantly enhances neuroimaging analysis for brain disorders. Machine learning and deep learning methods show superior performance in disease classification and lesion segmentation compared to traditional approaches.

Keywords:
Artificial intelligenceDeep learningMachine learningMental illnessNeuroimagingNeurological disease

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Data Analysis

Background:

  • Artificial intelligence (AI) is revolutionizing neuroimaging data analysis, improving our understanding of complex brain functions.
  • The integration of AI techniques offers potential advancements in diagnostic capabilities within neuroscience.

Purpose of the Study:

  • To investigate the impact of AI techniques on neuroimaging data analysis.
  • To enhance diagnostic capabilities and advance the field of AI-driven neuroimaging.

Main Methods:

  • A systematic literature search was performed across PubMed, IEEE Xplore, and Scopus from 2013 to 2023.
  • 456 articles on AI-driven neuroimaging analysis were curated, with 104 selected based on stringent inclusion criteria and quality assessments.
  • Studies focused on various neuroimaging modalities for mental and neurological disorders, employing precise data extraction protocols.

Main Results:

  • The review included 104 studies, with 19.2% focusing on mental illness and 80.7% on neurological disorders.
  • Key clinical applications identified were disease classification (58.7%) and lesion segmentation (28.9%).
  • Machine learning and deep learning algorithms demonstrated superior performance over traditional methods in neuroimaging analysis.

Conclusions:

  • AI-driven neuroimaging analysis holds significant potential for transforming both research and clinical applications.
  • The study highlights the effectiveness of AI, particularly machine learning and deep learning, in advancing neuroimaging diagnostics and understanding brain disorders.