A Novel Pupillometric-Based Application for the Automated Detection of ADHD Using Machine Learning

Attention-deficit/hyperactivity disorder is the most pervasive neurodevelopmental disorder among children and adolescents. Current clinical diagnosis, however, is inaccurate and inefficient, hindering the administration of proper treatment regimens. Clinical assessments are based on qualitative observations of perceived behavior. They are time-consuming and costly, preventing individuals from gaining the support they need to succeed academically, socially, and occupationally. A more accurate and efficient method of detection is necessary to ensure that all children are able to be diagnosed and given proper treatment regimens. This research proposes a novel machine learning-based method to analyze pupil-dynamics data as an objective biomarker to characterize ADHD. After visualizing and engineering pupillometric features, an evaluation of state-of-the-art machine learning algorithms showed that an Ensemble Voting Classifier yielded the optimal binary classification metrics using leave-one-out-cross-validation (LOOCV). The model classified ADHD with 82.1% sensitivity, 72.7% specificity, and 85.6% AUROC. Moreover, novel insights into associations between pupillometric features and the presence of ADHD were garnered and statistically validated. The optimal machine learning model was implemented in a web application that administers a memory task and captures pupil biometrics in real-time to output a probabilistic risk score of a patient having ADHD. This application is the first to use pupil-size dynamics as a biomarker, and offers a time-efficient and accurate approach to detect ADHD in children.