AI Prediction of Treatment Response in DME

NCT07707856 | Completed

• 1 other identifier: AI-DME-SR-2026
• Study Type: observational
• Target: 1,284
• Locations: 1 country
• Sites: 1

Brief 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

Diabetic Macular Edema (DME); Diabetic Retinopathy

Keywords

Diabetic Macular Edema; DME; Artificial Intelligence; Machine Learning

Outcome Measures

Primary Outcomes (1)

• Predictive Performance of the Artificial Intelligence Model for Treatment Response

  To evaluate the ability of the artificial intelligence model to predict treatment response in patients with diabetic macular edema using retinal imaging biomarkers. Model performance will be assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, recall, F1-score, and calibration, where applicable.

  Baseline to 12 months

Secondary Outcomes (1)

• Change in Central Retinal Thickness

  Baseline to 12 months

Study Arms (1)

Artificial Intelligence

This review focuses on published studies evaluating artificial intelligence (AI)-based models that use retinal imaging biomarkers to predict treatment response in patients with diabetic macular edema. The review synthesizes evidence on machine learning and deep learning approaches, imaging modalities including optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), and fundus photography, and their reported predictive performance for anatomical and functional treatment outcomes.

Other: Artificial Intelligence-Based Imaging Analysis

Interventions

Artificial Intelligence-Based Imaging Analysis OTHER

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.

Artificial Intelligence

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients diagnosed with diabetic macular edema (DME) who underwent retinal imaging and received treatment with intravitreal anti-vascular endothelial growth factor (anti-VEGF), intravitreal corticosteroids, laser photocoagulation, or combination therapy. Eligible participants have baseline and follow-up clinical data, including best-corrected visual acuity (BCVA), optical coherence tomography (OCT), and/or optical coherence tomography angiography (OCTA) images suitable for artificial intelligence-based analysis to predict anatomical and functional treatment response.

You may qualify if:

• Adults aged 18 years or older.
• Diagnosis of diabetic macular edema (DME) confirmed by clinical examination and optical coherence tomography (OCT).
• Treatment with intravitreal anti-vascular endothelial growth factor (anti-VEGF), intravitreal corticosteroids, focal/grid laser photocoagulation, or combination therapy.
• Availability of baseline retinal imaging, including OCT, OCT angiography (OCTA), fundus photography, or multimodal imaging suitable for artificial intelligence analysis.
• Availability of baseline and follow-up best-corrected visual acuity (BCVA) and central retinal thickness (CRT) measurements.
• Complete demographic and clinical data required for model development or validation.
• Minimum follow-up of 3 months after initiation of treatment.

You may not qualify if:

• Macular edema due to causes other than diabetes.
• Previous vitreoretinal surgery in the study eye.
• Coexisting retinal diseases that may affect visual or anatomical outcomes (e.g., retinal vein occlusion, age-related macular degeneration, uveitis, inherited retinal disorders).
• Significant media opacity resulting in poor-quality retinal imaging.
• Incomplete clinical records or missing imaging data.
• Follow-up duration less than 3 months.
• Images that fail quality-control criteria for artificial intelligence analysis.

Sponsors & Collaborators

• Benha University: lead

Study Sites (1)

Benha University

Banhā, Benha, 13111, Egypt

Location

MeSH Terms

Conditions

Diabetic Retinopathy

Condition Hierarchy (Ancestors)

Retinal Diseases; Eye Diseases; Diabetic Angiopathies; Vascular Diseases; Cardiovascular Diseases; Diabetes Complications; Diabetes Mellitus; Endocrine System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Lecturer of Ophthalmology

Study Record Dates

• First Submitted: July 10, 2026
• First Posted: July 16, 2026
• Study Start: May 15, 2025
• Primary Completion: June 22, 2025
• Study Completion: June 30, 2025
• Last Updated: July 16, 2026

Record last verified: 2026-07

Locations

EG (1)