A Deep Learning Approach to Imputation of Dynamic Pupil Size Data and Prediction of ADHD

Seongyune Choi, Yeonju Jang, Hyeoncheol Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adolescents. Traditional diagnosis methods of ADHD focus on observed behavior and reported symptoms, which may lead to a misdiagnosis. Studies have focused on computer-aided systems to improve the objectivity and accuracy of ADHD diagnosis by utilizing psychophysiological data measured from devices such as EEG and MRI. Despite their performance, their low accessibility has prevented their widespread adoption. We propose a novel ADHD prediction method based on the pupil size dynamics measured using eye tracking. Such data typically contain missing values owing to anomalies including blinking or outliers, which negatively impact the classification. We therefore applied an end-to-end deep learning model designed to impute the dynamic pupil size data and predict ADHD simultaneously. We used the recorded dataset of an experiment involving 28 children with ADHD and 22 children as a control group. Each subject conducted an eight-second visuospatial working memory task 160 times. We treated each trial as an independent data sample. The proposed model effectively imputes missing values and outperforms other models in predicting ADHD (AUC of 0.863). Thus, given its high accessibility and low cost, the proposed approach is promising for objective ADHD diagnosis.

Original languageEnglish
Article number2350020
JournalInternational Journal on Artificial Intelligence Tools
Issue number6
Publication statusPublished - 2023 Sept 1

Bibliographical note

Publisher Copyright:
© 2023 World Scientific Publishing Company.


  • Attention-deficit/hyperactivity disorder (ADHD)
  • deep learning
  • eye tracking
  • pupil size

ASJC Scopus subject areas

  • Artificial Intelligence


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