ArticleScientific reports2021
A Robust Machine Learning Based Framework for the Automated Detection of ADHD Using Pupillometric Biomarkers and Time Series Analysis.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 65 citations in OpenAlex.
- MEYELens: An affordable, open-source, 3D-printable eyewear platform for pupillometry and gaze tracking.Behavior research methods · 2026Article
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- The Pupil-Brain System at Rest: Spontaneous Pupil Fluctuations as Markers of Neuromodulatory and Network Dynamics.Psychophysiology · 2026Review
- Frequency Band Analysis of Multiple Stationary Time Series.Statistics in medicine · 2026Article
- Early attention-deficit/hyperactivity disorder (ADHD) with NeuroDCT-ICA and rhinofish optimization (RFO) algorithm based optimized ADHD-AttentionNet.Scientific reports · 2025Article
- Machine learning on a smartphone-based CPT for ADHD prediction.Frontiers in psychiatry · 2025Article
- Preliminary results of the EPIDIA4Kids study on brain function in children: multidimensional ADHD-related symptomatology screening using multimodality biometry.Frontiers in psychiatry · 2025Article
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Unlocking the potential of wearable technology: Fitbit-derived measures for predicting ADHD in adolescents.Frontiers in child and adolescent psychiatry · 2025Article
- Auxiliary Diagnosis of Children With Attention-Deficit/Hyperactivity Disorder Using Eye-Tracking and Digital Biomarkers: Case-Control Study.JMIR mHealth and uHealth · 2024Article
- Lévy Flight Model of Gaze Trajectories to Assist in ADHD Diagnoses.Entropy (Basel, Switzerland) · 2024Article
- Medication Usage Record-Based Predictive Modeling of Neurodevelopmental Abnormality in Infants under One Year: A Prospective Birth Cohort Study.Healthcare (Basel, Switzerland) · 2024Article
- Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA Interview.IEEE journal of translational engineering in health and medicine · 2024Article
- A data science approach to optimize ADHD assessment with the BRIEF-2 questionnaire.Translational neuroscience · 2024Article
- Early detection of paediatric and adolescent obsessive-compulsive, separation anxiety and attention deficit hyperactivity disorder using machine learning algorithms.Health information science and systems · 2023Article
- Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms.Translational psychiatry · 2023Review
- Delineating early developmental pathways to ADHD: Setting an international research agenda.JCPP advances · 2023Article
- Data-Driven Methods for Predicting ADHD Diagnosis and Related Impairment: The Potential of a Machine Learning Approach.Research on child and adolescent psychopathology · 2023Article
- Big data, machine learning, and population health: predicting cognitive outcomes in childhood.Pediatric research · 2023Review
- Can biomarkers be used to diagnose attention deficit hyperactivity disorder?Frontiers in psychiatry · 2023Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and efficient detection of attention-deficit/hyperactivity disorder (ADHD) is critical to ensure proper treatment for affected individuals. Current clinical examinations, however, are inefficient and prone to misdiagnosis, as they rely on qualitative observations of perceived behavior. We propose a robust machine learning based framework that analyzes pupil-size dynamics as an objective biomarker for the automated detection of ADHD. Our framework integrates a comprehensive pupillometric feature engineering and visualization pipeline with state-of-the-art binary classification algorithms and univariate feature selection. The support vector machine classifier achieved an average 85.6% area under the receiver operating characteristic (AUROC), 77.3% sensitivity, and 75.3% specificity using ten-fold nested cross-validation (CV) on a declassified dataset of 50 patients. 218 of the 783 engineered features, including fourier transform metrics, absolute energy, consecutive quantile changes, approximate entropy, aggregated linear trends, as well as pupil-size dilation velocity, were found to be statistically significant differentiators (p < 0.05), and provide novel behavioral insights into associations between pupil-size dynamics and the presence of ADHD. Despite a limited sample size, the strong AUROC values highlight the robustness of the binary classifiers in detecting ADHD-as such, with additional data, sensitivity and specificity metrics can be substantially augmented. This study is the first to apply machine learning based methods for the detection of ADHD using solely pupillometrics, and highlights its strength as a potential discriminative biomarker, paving the path for the development of novel diagnostic applications to aid in the detection of ADHD using oculometric paradigms and machine learning.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.