Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
November 30, 2024Journal of Student ResearchOpen Access

Multi-Resolution Image Features of Retinal Images and Optic Nerve Head for Biomarker Identification in Attention-Deficit/Hyperactivity Disorder

View Full Paper
Ask AI
Bookmark
Share

Authors

THTaehyeon HwangEMElizabeth McCook

Discussion

Loading...

Member takes

Overview

Machine learning identifies key biomarkers in ADHD through retinal images and optic nerve head segmentation, suggesting a new diagnostic approach.

Key Points

  • The proposed system achieved a high accuracy of 86.88% in diagnosing ADHD using retinal images.
  • Optic nerve head segmentation was utilized to improve the model's understanding of ADHD biomarkers.
  • Feature extraction techniques helped capture essential data patterns relevant to ADHD.
  • Early diagnosis using this method may enhance treatment outcomes for individuals with ADHD.

Cite This Study

Hwang et al. (2024) studied this question.

synapsesocial.com/papers/68af659bad7bf08b1eae574fhttps://doi.org/10.47611/jsrhs.v13i4.8332
View Full Paper
Ask AI
Bookmark
Share

Also Consider

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

  1. 1A Two-Stage Machine Learning Approach for Enhancing Attention-Deficit/Hyperactivity Disorder Diagnostic Accuracy: Optic Disc Segmentation and Symptom Severity Classification2025
  2. 2Rapid Screening of Attention-Deficit/Hyperactivity Disorder using Fundus Photography with Retinal Vessel and Optic Disc Morphology2025
  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