Machine Learning-Driven Analysis of Temporal Pupil Dynamics for Interpretable ADHD Diagnosis (Preprint)
Authors: Sharma Swati , Chakrabarty Mrinmoy , Ray Sonia Baloni , Shukla Jainendra 
Published: February 2025

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by persistent patterns of inattention and hyperactivity. Current diagnostic methods rely heavily on subjective measures, such as clinical interviews and behavior rating scales, which are prone to bias and variability. Objective biomarkers, essential for reliable and standardized diagnosis, remain elusive. Pupillometry, which measures dynamic pupil responses associated with cognitive and attentional processes, offers a promising avenue for objective ADHD diagnostics. However, existing studies often overlook clinically relevant features and fail to prioritize model interpretability, hindering their potential for clinical implementation.

OBJECTIVE

This study aims to develop interpretable machine learning models utilizing temporal pupil dynamics to classify ADHD and control groups, aiming for improved diagnostic accuracy and model explainability.

METHODS

This study utilized already published pupillometry data from 49 participants, including 21 controls and 28 ADHD-diagnosed children, with 17 assessed both on-medication and off-medication. Data were collected during a visuospatial working memory task designed to evaluate attentional processes. The data were preprocessed to remove noise and standardized for analysis. Pupil and behavioral features were first identified based on literature review of pupillometry studies conducted with the ADHD population. The final set of features was determined through statistical analyses using mixed and block-wise ANOVA to assess significance. Binary classification models were developed to differentiate ADHD from control participants. The models were evaluated progressively, starting with features derived only from pupil dynamics, then incorporating task performance metrics, and finally including reaction time metrics. Performance was assessed using accuracy and the area under the receiver operating characteristic curve.

RESULTS

The study ensured interpretability through the selection of statistically significant features, supported by literature review, that contribute meaningfully to the classification task. Key features included median pupil size (blocks 1 and 3), pupil dilation and contraction rates (blocks 4 and 8), reaction time, and task performance metrics, with each exhibiting distinct patterns of significance. Visualizations such as heatmaps and feature importance charts highlighted their relevance, providing transparency in the models' decision-making processes.
The classification models demonstrated robust performance using these interpretable features. Models trained exclusively on pupil features achieved a best accuracy of 86.7% with an AUROC score of 0.884. Incorporating task performance metrics improved performance, achieving a best accuracy of 88.9% with an AUROC score of 0.931. The final integration of reaction time metrics resulted in the highest performance, achieving a best accuracy of 90%, an AUROC score of 0.93, sensitivity of 100%, and specificity of 80.8%.

CONCLUSIONS

This study highlights the potential of leveraging temporal pupil dynamics as an objective biomarker for ADHD. By focusing on interpretable and clinically relevant features, the proposed models offer a practical and trustworthy approach to ADHD diagnosis, advancing the development of diagnostic tools for clinical use.