Machine Learning-Based Muscle Response Analysis for Diabetic Retinopathy: Trial Dynamics and Diagnostic Value

Diabetic retinopathy (DR) is a major cause of vision impairment worldwide, and early diagnosis remains a key challenge. Building upon previous work that demonstrated the utility of machine learning (ML) in classifying DR patients based on electromyographic (EMG) muscle response to visual stimuli, this study extends the investigation by analyzing intertrial dynamics, test duration efficiency, and subject-aware cross-trial transferability. A random forest (RF) classifier was trained on muscle response data collected from healthy individuals, untreated DR patients, and laser-treated patients, each exposed to structured visual stimuli across multiple trials. The study evaluates classification performance under varying conditions: different acquisition lengths, reduced test durations, intertrial transfer scenarios within the same subject, and in the presence of measurement uncertainty. Results confirm that patient class and stimulus type can be reliably predicted even with short-duration acquisitions, highlighting that early EMG signal segments encode sufficient discriminative information. Additionally, performance trends across trials provide insights into response stability and intrasubject adaptation effects over time. This work reinforces the potential of ML-assisted neuromuscular analysis in DR diagnosis within controlled, subject-aware measurement scenarios and suggests pathways for more efficient, personalized screening protocols.