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Related Concept Videos

Brain Imaging01:14

Brain Imaging

211
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...
211

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Predictive and Explainable Artificial Intelligence for Neuroimaging Applications.

Sekwang Lee1, Kwang-Sig Lee2

  • 1Department of Physical Medicine and Rehabilitation, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea.

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This summary is machine-generated.

Predictive and explainable artificial intelligence show promise in neuroimaging, offering a non-invasive decision support system. These AI models achieve high accuracy in classifying brain conditions and predicting disease progression.

Keywords:
explainable artificial intelligenceneuroimagingpredictive artificial intelligence

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Advancements in artificial intelligence (AI) are transforming neuroimaging.
  • Predictive and explainable AI offer new capabilities for analyzing brain images and associated diseases.

Purpose of the Study:

  • To review new advances in predictive and explainable AI for neuroimaging.
  • To highlight the performance and key predictors identified by AI models in neuroimaging studies.

Main Methods:

  • Systematic review of 30 PubMed studies published from 2019 onwards.
  • Search terms included 'neuroimaging' combined with 'machine learning' or 'deep learning'.
  • Studies were selected based on participant data, AI interventions, and performance outcomes (accuracy, AUC).

Main Results:

  • AI models demonstrated high performance, with accuracy ranging from 58-96% and AUC from 70-98%.
  • Support vector machines and convolutional neural networks showed top performance in classification tasks.
  • Random forests excelled in regression tasks, and various demographic, health, and neuroimaging factors were identified as key predictors.

Conclusions:

  • Predictive and explainable AI serve as effective, non-invasive decision support systems in neuroimaging.
  • These AI approaches enhance the analysis and understanding of brain images and associated diseases.