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Updated: Aug 14, 2025

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Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

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Journal of Attention Disorders
|January 18, 2023
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Machine learning (ML) shows promise for diagnosing attention-deficit/hyperactivity disorder (ADHD) using MRI scans. However, current methods need refinement for reliable clinical use.

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Psychiatry

Background:

  • Attention-deficit/hyperactivity disorder (ADHD) diagnosis relies on clinical assessment.
  • Magnetic resonance imaging (MRI) offers objective biomarkers for neurological conditions.
  • Machine learning (ML) models are increasingly explored for diagnostic classification in ADHD using MRI data.

Approach:

  • Systematic literature review of MRI-based diagnostic classifiers for ADHD.
  • Data extraction on MRI modalities, ML models, and sample characteristics.
  • Analysis of classification accuracies, cross-validation, and testing methodologies.

Key Points:

  • Studies report varied classification accuracies due to diverse MRI techniques, ML models, and validation strategies.
  • Cross-validation inflates performance estimates; held-out test accuracies are more generalizable.
  • Test accuracies improved over time, linked to advanced ML methods and data balancing, not sample size.

Conclusions:

  • Current ML-based MRI classifiers for ADHD lack demonstrated clinical utility.
  • Future development requires large, multi-modal imaging datasets.
  • Integrating cognitive and genetic data may enhance ADHD classification tools.