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February 28, 2025Journal of Student ResearchOpen Access

Rapid Screening of Attention-Deficit/Hyperactivity Disorder using Fundus Photography with Retinal Vessel and Optic Disc Morphology

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Authors

JLJaewon LeeFrederick National Laboratory for Cancer ResearchSLSherrie Lah

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Overview

Machine learning predicts ADHD with 88.15% accuracy using fundus photography, highlighting new diagnostic potential.

Key Points

  • The model achieved an accuracy of 88.15%, demonstrating rapid and effective ADHD screening.
  • Key features from fundus photography, like retinal vessels and optic disc shape, were analyzed.
  • A convolutional neural network model was implemented, showcasing its capability in ADHD detection.
  • This approach indicates a promising future for accessible and accurate ADHD diagnostics through innovative technology.

Cite This Study

Lee et al. (2025) studied this question.

synapsesocial.com/papers/68af5f0dad7bf08b1eae19c6https://doi.org/10.47611/jsrhs.v14i1.8494
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Multi-Resolution Image Features of Retinal Images and Optic Nerve Head for Biomarker Identification in Attention-Deficit/Hyperactivity Disorder2024
  2. 2A Two-Stage Machine Learning Approach for Enhancing Attention-Deficit/Hyperactivity Disorder Diagnostic Accuracy: Optic Disc Segmentation and Symptom Severity Classification2025
  3. 3Retinal Image Analysis for Simultaneous Classification and Severity Grading of Attention-Deficit Hyperactivity Disorder and Autism Spectrum Disorder using Deep Learning2024
  4. 4Early Detection of ADHD in Children Using Two Learning Approaches2024
  5. 5Development of an innovative approach using portable eye tracking to assist ADHD screening: a machine learning study2024 · 26 citations