Electroretinograms (ERGs) show differences between typically developing populations and those with a diagnosis of autism spectrum disorder (ASD) or attention deficit/hyperactivity disorder (ADHD). In a series of ERGs collected in ASD (n = 77), ADHD (n = 43), ASD + ADHD (n = 21), and control (n = 137) groups, this analysis explores the use of machine learning and feature selection techniques to improve the classification between these clinically defined groups. Standard time domain and signal analysis features were evaluated in different machine learning models. For ASD classification, a balanced accuracy (BA) of 0.87 was achieved for male participants. For ADHD, a BA of 0.84 was achieved for female participants. When a three-group model (ASD, ADHD, and control) the BA was lower, at 0.70, and fell further to 0.53 when all groups were included (ASD, ADHD, ASD + ADHD, and control). The findings support a role for the ERG in establishing a broad two-group classification of ASD or ADHD, but the model's performance depends upon sex and is limited when multiple classes are included in machine learning modeling.

Behavioural and Brain Sciences Unit Population Policy and Practice Programme UCL Great Ormond Street Institute of Child Health University College London London WC1N 1EH UK

Biomedical Engineering Department University of Connecticut Storrs CT 06269 USA

Caring Futures Institute College of Nursing and Health Sciences Flinders University Adelaide 5000 SA Australia

College of Psychology and Education Flinders University Adelaide 5000 SA Australia

Institute of Computer Science of the Czech Academy of Sciences Pod Vodarenskou Vezi 2 182 00 Prague Czech Republic

National Institute of Public Health Srobarova 48 100 00 Prague Czech Republic

Pattern Recognition Lab Friedrich Alexander Universität Erlangen Nürnberg 91058 Erlangen Germany

The Tony Kriss Visual Electrophysiology Unit Clinical and Academic Department of Ophthalmology Great Ormond Street Hospital for Children NHS Trust London WC1N 3BH UK

UCL Great Ormond Street Institute of Child Health University College London London WC1N 1EH UK

VisioMed AI Golovinskoe Highway 8 2A 125212 Moscow Russia

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