Diabetic RetinopathyNCT07707856
AI Prediction of Treatment Response in DME
An observational study of Diabetic Retinopathy and Diabetic Macular Edema (DME), run by Benha University at 1 site in Egypt.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Diabetic Retinopathy, Diabetic Macular Edema (DME)
- What is being tested?
- Artificial Intelligence-Based Imaging Analysis
- How many people
- 1,284 participants
- Where
- 1 siteEgypt
- Study dates
- 15 May 2025to 30 Jun 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Artificial Intelligence and Imaging Biomarkers for Predicting Treatment Response in Diabetic Macular Edema: A Systematic Review
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.
Read the detailed description
Diabetic macular edema is one of the most common causes of visual impairment in patients with diabetic retinopathy. Despite the widespread use of intravitreal anti-VEGF agents, corticosteroids, laser photocoagulation, and combination therapies, individual responses remain highly variable. Early identification of patients who are likely to respond to specific treatments may improve visual outcomes while reducing unnecessary treatment burden.
Advances in retinal imaging, particularly optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA), have enabled detailed characterization of retinal structural and vascular biomarkers associated with treatment outcomes. Commonly investigated biomarkers include central retinal thickness, intraretinal cysts, subretinal fluid, hyperreflective retinal foci, disorganization of the retinal inner layers (DRIL), ellipsoid zone integrity, external limiting membrane integrity, choroidal thickness, choroidal vascularity index, retinal fluid volume, vascular density, and foveal avascular zone parameters.
Artificial intelligence techniques, including conventional machine learning and deep learning algorithms, increasingly integrate retinal imaging features with demographic and clinical variables to predict functional and anatomical responses to therapy. Reported prediction models have demonstrated promising diagnostic performance, although considerable variability exists regarding imaging modalities, model architecture, validation strategies, outcome definitions, and reporting standards.
This systematic review will comprehensively evaluate published studies investigating AI-based prediction of treatment response in patients with diabetic macular edema. The review will identify imaging biomarkers contributing to predictive performance, compare machine learning and deep learning approaches, evaluate different retinal imaging modalities, summarize reported model performance metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1-score, and assess methodological quality using validated risk-of-bias assessment tools. Where sufficient homogeneous data are available, a random-effects meta-analysis will be conducted to quantitatively synthesize model performance.
The findings are expected to identify robust imaging biomarkers, highlight current limitations of AI prediction models, and provide recommendations for future research and clinical implementation of AI-assisted precision medicine in diabetic macular edema.
- Conditions
- Diabetic Retinopathy, Diabetic Macular Edema (DME)
- Being tested
- Artificial Intelligence-Based Imaging Analysis
- Keywords
- Diabetic Macular Edema, DME, Artificial Intelligence, Machine Learning
- Medical subject headings
- Diabetic Retinopathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence-Based Imaging AnalysisOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Artificial Intelligence-Based Imaging Analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline to 12 months
Secondary outcomes (1)
Change in Central Retinal Thickness
Reduction in central retinal thickness measured by optical coherence tomography and its relationship with AI prediction results.
Measured: Baseline to 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jul 2026
- Study start15 May 2025
- Primary completion22 Jun 2025
- Study completion30 Jun 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetic RetinopathyStudies of the same condition, open and under way.
- More trials run by Benha UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 1,284 participants (actual)
Sponsor and investigators
- Lead sponsor
- Benha University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ehab Mohamed Elsayed Mohamed Saad, Benha University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07707856
- Sponsor's protocol ID
- AI-DME-SR-2026
- Status last verified
- Jul 2026
- First submitted
- 10 Jul 2026
- Last updated
- 16 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence and Imaging Biomarkers for Predicting Treatment Response in Diabetic Macular Edema: A Systematic Review
- ClinicalTrials.gov ID
- NCT07707856
- Acronym / sponsor ID
- AI-DME-SR-2026
- Conditions
- Diabetic Retinopathy, Diabetic Macular Edema (DME)
- Keywords
- Diabetic Macular Edema, DME, Artificial Intelligence, Machine Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,284 participants (actual)
- Lead sponsor
- Benha UniversityUniversity, hospital or other
- Study start
- 15 May 2025 (actual)
- Primary completion
- 22 Jun 2025 (actual)
- Study completion
- 30 Jun 2025 (actual)
- First posted
- 16 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Diabetic macular edema is one of the most common causes of visual impairment in patients with diabetic retinopathy. Despite the widespread use of intravitreal anti-VEGF agents, corticosteroids, laser photocoagulation, and combination therapies, individual responses remain highly variable. Early identification of patients who are likely to respond to specific treatments may improve visual outcomes while reducing unnecessary treatment burden.
Advances in retinal imaging, particularly optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA), have enabled detailed characterization of retinal structural and vascular biomarkers associated with treatment outcomes. Commonly investigated biomarkers include central retinal thickness, intraretinal cysts, subretinal fluid, hyperreflective retinal foci, disorganization of the retinal inner layers (DRIL), ellipsoid zone integrity, external limiting membrane integrity, choroidal thickness, choroidal vascularity index, retinal fluid volume, vascular density, and foveal avascular zone parameters.
Artificial intelligence techniques, including conventional machine learning and deep learning algorithms, increasingly integrate retinal imaging features with demographic and clinical variables to predict functional and anatomical responses to therapy. Reported prediction models have demonstrated promising diagnostic performance, although considerable variability exists regarding imaging modalities, model architecture, validation strategies, outcome definitions, and reporting standards.
This systematic review will comprehensively evaluate published studies investigating AI-based prediction of treatment response in patients with diabetic macular edema. The review will identify imaging biomarkers contributing to predictive performance, compare machine learning and deep learning approaches, evaluate different retinal imaging modalities, summarize reported model performance metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1-score, and assess methodological quality using validated risk-of-bias assessment tools. Where sufficient homogeneous data are available, a random-effects meta-analysis will be conducted to quantitatively synthesize model performance.
The findings are expected to identify robust imaging biomarkers, highlight current limitations of AI prediction models, and provide recommendations for future research and clinical implementation of AI-assisted precision medicine in diabetic macular edema.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence | – | Other: Artificial Intelligence-Based Imaging AnalysisThis 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (7 listed)
- 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.
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence-Based Imaging AnalysisParticipants 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. | Other | Artificial Intelligence |
Primary outcome
Predictive Performance of the Artificial Intelligence Model for Treatment Response
Time frame: Baseline to 12 months
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.
1 secondary outcome
Change in Central Retinal Thickness
Time frame: Baseline to 12 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Jul 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetic Retinopathy and Diabetic Macular Edema (DME), run by Benha University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 16 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive Performance of the Artificial Intelligence Model for Treatment Response (time frame: Baseline to 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Jul 2026.