Development of an innovative approach using portable eye tracking to assist ADHD screening: a machine learning study.

ABSTRACT:

Introduction

Attention-deficit/hyperactivity disorder (ADHD) affects a significant proportion of the pediatric population, making early detection crucial for effective intervention. Eye movements are controlled by brain regions associated with neuropsychological functions, such as selective attention, response inhibition, and working memory, and their deficits are related to the core characteristics of ADHD. Herein, we aimed to develop a screening model for ADHD using machine learning (ML) and eye-tracking features from tasks that reflect neuropsychological deficits in ADHD.

Methods

Fifty-six children (mean age 8.38 ± 1.58, 45 males) diagnosed with ADHD based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition were recruited along with seventy-nine ty

SUBMITTER: Yoo JH 

PROVIDER: S-EPMC10902460 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature