ArticlemedRxiv : the preprint server for health sciences2025
Mobile Objective Diagnostics of Macular Degeneration using Dark-Adapted Visual Evoked Potentials.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Purpose: Delayed Dark-Adapted vision Recovery (DAR) is a known biomarker for Age-related Macular Degeneration (AMD); however, its measurement is often cumbersome for both patients and examiners. In this study, we developed NeuroVEP, a portable, wireless, and user-friendly system designed to objectively assess Dark-Adapted Visual Evoked Potentials (DAVEP). Methods: NeuroVEP consists of a headset with a smartphone that delivers controlled photo-bleach and monocular pattern reversal stimuli while utilizing custom electroencephalography (EEG) electrodes and electronics to measure DAVEP. The system allows for separate analysis of the near peripheral and macular visual field of each eye, completing the test in a comfortable, single-session format (<25 minutes) without requiring subjective patient feedback.The NeuroVEP test protocol included: (i) Mesopic luminance pattern reversal VEP for macular and peripheral regions (5 mins), (ii) Full-field photopic pattern reversal VEP (2.5 mins), (iii) Scotopic luminance DAVEP recovery post photo-bleach (up to 15 mins), measured simultaneously from both eyes.The data were analyzed for 66 participants, divided into four cohorts: (A) Age-matched healthy controls with no ophthalmic pathologies (n=10), (B) Early-stage AMD (AREDS1) (n=19), (C) Intermediate-stage AMD (AREDS3) (n=18), (D) Advanced-stage AMD (AREDS4/5) (n=19).Advanced signal processing and machine learning methodologies were applied to filter and process the VEP responses from the DAR segment of the experiment. 13 discriminating features were extracted from the processed signals and classified for each participant using a Bayesian statistical framework and Gaussian Mixture Model (GMM). Results: The algorithm demonstrated: 86% accuracy in early-stage AMD detection (Healthy vs. Early AMD) (Sensitivity: 97%, Specificity: 65%, AUC-ROC: 0.81 and AUC-PR: 0.92) and 93% accuracy in overall AMD detection (Healthy vs. All AMD stages) (Sensitivity: 98%, Specificity: 65%, AUC-ROC: 0.82 and AUC-PR: 0.97). Conclusions: We successfully developed a portable, objective user-friendly VEP system and an advanced Bayesian-GMM statistical analysis framework capable of identifying DAR deficits in AMD patients. This novel technology shows high potential for early AMD detection and could serve as a non-invasive, objective diagnostic tool for AMD screening in clinical and remote settings.
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Registered trials
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