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Related Experiment Video

Updated: Sep 11, 2025

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

Machine learning-driven analysis of temporal pupil dynamics for interpretable ADHD diagnosis.

Swati Sharma¹, Mrinmoy Chakrabarty¹, Sonia Baloni Ray¹

¹Indraprastha Institute of Information Technology, Delhi, India.

Computers in Biology and Medicine

|August 15, 2025

Summary

This summary is machine-generated.

This study introduces an interpretable machine learning framework using pupil dynamics to objectively diagnose attention-deficit/hyperactivity disorder (ADHD). The novel approach achieves high accuracy, paving the way for more reliable ADHD assessments.

Keywords:

Attention-deficit/hyperactivity disorder Deep learning Machine learning Mental health Neurodevelopment Pupillometry

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Published on: November 26, 2019

Area of Science:

Neuroscience
Computational Psychiatry

Background:

Attention-deficit/hyperactivity disorder (ADHD) diagnosis relies on subjective assessments, lacking objective biomarkers.
Pupillometry offers a potential objective measure of cognitive and attentional processes relevant to ADHD.

Purpose of the Study:

To develop an interpretable machine learning framework using temporal pupil dynamics for ADHD classification.
To identify and validate novel, clinically relevant pupil-based features for objective ADHD diagnosis.

Main Methods:

Analyzed pupillometry data from 49 participants (21 controls, 28 ADHD) during a visuospatial working memory task.
Extracted novel dynamic pupil dilation and constriction rates in temporal segments as key features.
Employed statistical analyses (mixed ANOVA) and interpretable machine learning models, prioritizing validated features.

Main Results:

The framework achieved high classification accuracy: 84.4% using pupil features alone, 86.7% with task performance, and 88.9% with reaction time.
Models demonstrated strong performance with an AUROC of up to 91.5% and high sensitivity (97.8%) when including reaction time.

Conclusions:

Interpretable, dynamic pupil metrics derived from temporal dynamics offer a promising avenue for objective and reproducible ADHD diagnosis.
This approach has the potential to advance clinical deployment and improve the standardization of ADHD diagnostic procedures.