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The Retina

The Retina

The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.

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Updated: May 21, 2025

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Retinal fundus imaging as biomarker for ADHD using machine learning for screening and visual attention

Hangnyoung Choi^1,2, JaeSeong Hong³, Hyun Goo Kang^4,5

¹Department of Child and Adolescent Psychiatry, Autism and Developmental Disorder Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.

NPJ Digital Medicine

|March 18, 2025

Summary

Retinal fundus photographs analyzed with machine learning show promise for noninvasively screening attention-deficit/hyperactivity disorder (ADHD). This approach may also help stratify executive function deficits, particularly in visual attention domains.

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Last Updated: May 21, 2025

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Published on: December 7, 2018

Area of Science:

Ophthalmology
Neurodevelopmental Disorders
Artificial Intelligence in Medicine

Background:

Attention-deficit/hyperactivity disorder (ADHD) presents diagnostic challenges due to symptom heterogeneity.
Current ADHD diagnosis relies on clinical assessment, lacking objective biomarkers.
Executive function (EF) deficits are a core feature of ADHD, varying in severity.

Purpose of the Study:

To investigate machine learning (ML) analysis of retinal fundus photographs as a noninvasive biomarker for ADHD screening.
To evaluate the potential of retinal imaging for stratifying executive function (EF) deficits in ADHD.
To explore the utility of specific retinal features in predicting ADHD and EF subtypes.

Main Methods:

Retinal fundus photographs from 323 children and adolescents with ADHD and controls were analyzed using the AutoMorph pipeline.
Four machine learning models were employed for ADHD screening and EF subdomain prediction.
Shapely additive explanation (SHAP) method was used to interpret model predictions.

Main Results:

ADHD screening models achieved high diagnostic accuracy, with an area under the receiver operating characteristic curve (AUROC) ranging from 95.5% to 96.9%.
Stratification of EF deficits showed strong performance for the visual subdomain (AUROC > 85%) but weaker performance for the auditory subdomain.
Retinal feature analysis identified potential biomarkers for ADHD and visual attention deficits.

Conclusions:

Retinal fundus photography combined with ML offers a promising noninvasive tool for ADHD screening.
This approach demonstrates potential for stratifying executive function deficits, particularly in the visual attention domain.
Further research can validate retinal imaging as a clinical aid for ADHD assessment and management.