Our Research
Our research combines ocular imaging, artificial intelligence, biomarker discovery, and digital health to improve the detection, monitoring, and understanding of eye disease. We focus on connecting retinal changes with visual function and developing informative measures for research and clinical trials.
Natural History Studies of Macular Degeneration
Description: At the Sepah Lab, we conduct comprehensive longitudinal investigations documenting disease progression patterns in age-related macular conditions. These studies systematically track the natural course of macular degeneration, aiming to identify critical transition points in disease development and potentially reveal optimal windows for therapeutic intervention.
Structure-Function Correlation in Inherited Retinal Disorders
Description: Our lab advances research in structure-function relationships across various inherited retinal conditions. By meticulously mapping the correlation between structural changes observed through advanced imaging and functional visual outcomes, we aim to develop more sensitive metrics for disease monitoring and treatment response assessment.
Biomarker Discovery for Retinal Diseases
Description:Â We investigate molecular markers of inflammation and imaging biomarkers to better understand retinal disease. Our work examines how these markers relate to disease progression and treatment response, supporting the development of more informative approaches to disease assessment and monitoring.
Telehealth and Remote Vision Assessment Technologies
Description: Our lab is at the forefront of telehealth innovation, developing novel devices for at-home visual acuity measurement and monitoring. These technologies promise to transform patient care by providing reliable, frequent assessments outside traditional clinical settings, improving accessibility and continuity of care.
Wearable Technology for Ocular Health
Description: We are exploring the integration of wearable health devices to continuously track relevant physiological parameters that may influence ocular disease progression. This research aims to leverage everyday technology to gather valuable data that can inform both clinical practice and patient self-management.
Artificial Intelligence for Ophthalmic Diagnostics
Description:Â We investigate how ocular imaging and artificial intelligence can support the assessment of concerning choroidal lesions. Our research combines ultra-widefield imaging with deep learning and human review to help identify lesions that may require specialist evaluation.This work explores how computational tools can support clinical assessment and referral decisions.
Starguage: Automated Measurement in Stargardt Disease
Description:Our work on Starguage advances automated measurement of areas of reduced autofluorescence in Stargardt disease. Its highly repeatable measurements closely agree with expert manual grading, helping researchers assess retinal lesions more consistently.
This research supports more standardized measurement of disease-related retinal changes.
MyHealth: Understanding Patient Communication
Description: The MyHealth study explores how artificial intelligence can help researchers better understand patients’ concerns and care needs. We examine secure patient messages to characterize clinical concerns, administrative requests, and differences in communication across patient groups.
This work can inform more responsive patient communication and the organization of care.
Visual Function Assessment Using Pupillary Responses and EEG
Description: We investigate pupillary light responses and electroencephalography (EEG) as approaches to assessing visual function with less dependence on patients’ reported responses.
This research explores how physiological measurements can contribute to our understanding and assessment of vision.