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AI Prediction of Treatment Response in DME

NCT07707856 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1284

Last updated 2026-07-16

No results posted yet for this study

Summary

Diabetic macular edema (DME) is a leading cause of vision loss among individuals with diabetes mellitus. Although intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy is the standard treatment for center-involved DME, treatment response varies considerably between patients. Recent advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have enabled automated analysis of retinal imaging biomarkers to predict anatomical and functional treatment outcomes. This study aims to systematically evaluate published evidence regarding AI-based prediction models and imaging biomarkers used to predict treatment response in patients with DME. The review will assess the predictive performance of AI models, identify the most important imaging biomarkers, compare different AI approaches and imaging modalities, and summarize methodological strengths, limitations, and research gaps to support future development of precision ophthalmology.

Conditions

Interventions

OTHER

Artificial Intelligence-Based Imaging Analysis

Participants undergo retinal imaging analysis using artificial intelligence-based prediction models developed from optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), fundus photography, and relevant clinical variables. The AI algorithms are used to predict treatment response in diabetic macular edema, including anatomical and functional outcomes following anti-vascular endothelial growth factor (anti-VEGF), corticosteroid, laser, or combination therapies. The AI analysis does not alter clinical management and is performed for predictive evaluation only.

Sponsors & Collaborators

  • Benha University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-05-15
Primary Completion
2025-06-22
Completion
2025-06-30

Countries

  • Egypt

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07707856 on ClinicalTrials.gov