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February 28, 2025Journal of Student ResearchOpen Access

A Two-Stage Machine Learning Approach for Enhancing Attention-Deficit/Hyperactivity Disorder Diagnostic Accuracy: Optic Disc Segmentation and Symptom Severity Classification

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Authors

IPIrene ParkWRWilliam Rosser

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Overview

A two-stage machine learning system improves diagnostic accuracy of ADHD symptom severity classification from optic disc images, suggesting a novel assessment approach.

Key Points

  • The proposed machine learning system achieved an accuracy of 85.62% for diagnosing ADHD severity using optic disc images.
  • Key evidence includes the high accuracy level in diagnosing ADHD, with a direct correlation to optic disc segmentation.
  • This approach employs a two-stage machine learning model that analyzes fundus images to classify ADHD severity effectively.
  • The findings may streamline ADHD diagnostics, highlighting the need for innovative, technology-driven methods.

Cite This Study

Park et al. (2025) studied this question.

synapsesocial.com/papers/68af5f0dad7bf08b1eae19cfhttps://doi.org/10.47611/jsrhs.v14i1.8498
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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. 2Rapid Screening of Attention-Deficit/Hyperactivity Disorder using Fundus Photography with Retinal Vessel and Optic Disc Morphology2025
  3. 3Development of an innovative approach using portable eye tracking to assist ADHD screening: a machine learning study2024 · 26 citations
  4. 4AN AI-BASED SCREENING SYSTEM FOR ADHD IN REAL-WORLD CLINICAL2025
  5. 5Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder2023 · 22 citations