Multimodal Database of Retinal Images in Africa (MoDRIA) platform with 38 codebook variables.
The Multimodal Database of Retinal Images in Africa (MoDRIA) is a comprehensive dataset developed to drive advancements in machine learning and artificial intelligence for ophthalmological research. It addresses a critical gap in the availability of diverse and representative retinal imaging datasets from African populations, helping to reduce bias and improve the accuracy of AI-driven eye disease diagnosis. MoDRIA combines high-quality retinal images with detailed clinical data, reflecting real-world clinical environments across the African continent. This resource provides a robust foundation for training, validating, and benchmarking AI models, ultimately supporting the delivery of more equitable and effective ophthalmological care.
The dataset comprises high-resolution retinal images collected from patients across multiple eye care centers in Africa, accompanied by comprehensive metadata that includes demographics, clinical history, and diagnostic details. Each image has been expertly labelled by certified ophthalmologists, ensuring both diagnostic accuracy and research reliability. By capturing data from diverse patient populations across different African regions, the dataset provides a representative foundation for developing inclusive and unbiased AI models in ophthalmology.
All images undergo rigorous quality control procedures including standardized imaging protocols, expert validation, and comprehensive metadata verification. The dataset adheres to international standards for medical imaging research and patient privacy protection. Quality metrics include image resolution standards, annotation consistency, and clinical data validation protocols.
This dataset supports the development of AI-driven diagnostic tools for diabetic retinopathy, glaucoma, age-related macular degeneration, and other eye diseases. The focus on African populations helps address healthcare disparities and supports the development of more inclusive AI models for global ophthalmological care, particularly in resource-limited settings where early detection can prevent blindness. MoDRIA enables multi-modal research by linking retinal imaging, clinical assessments, laboratory results, and treatment outcomes for comprehensive ophthalmological research.
A list of all variables from the MoDRIA dataset with detailed specifications for ophthalmological research
| Variable Name | Description | Data Encoding/Codes |
|---|---|---|
| IMG_ID | Image ID number | Image identifier |
| Pt_ID | Patient ID number | Patient identifier |
| Camera | Camera type | |
| IMG_OD_or_OS | Image of the Right or Left eye | OD= right eye, OS = left eye |
| IMG_Disc_or_Mac_Center | Image with disc or macula centered | D= disc center, M= macula center |
| Age | Age | |
| Gender | Gender | 0= male, 1= female, nc = not collected |
| VAOD_Numerator | Numerator of visual acuity, right eye e.g., 6/18 | e.g., 6/12 or text: HM = hand motion PL = perception of light, NPL = no perception of light, nc = not collected |
| VAOD_Denominator | Denominator of visual acuity, right eye, e.g., 6/18 | e.g., 6/12 or text: HM = hand motion PL = perception of light, NPL = no perception of light, nc = not collected |
| VAOS_Numerator | Numerator of visual acuity, left eye e.g., 6/18 | e.g., 6/12 or text: HM = hand motion PL = perception of light, NPL = no perception of light, nc = not collected |
| VAOS_Denominator | Denominator of visual acuity, left eye, e.g., 6/18 | e.g., 6/12 or text: HM = hand motion PL = perception of light, NPL = no perception of light, nc = not collected |
| IOP_OD | Intraocular pressure, right eye | ICARE |
| IOP_OS | Intraocular pressure, left eye | ICARE |
| Weight | Weight | |
| Height | Height | |
| BMI | BMI | |
| SBP | Systolic blood pressure | type of device |
| DBP | Diastolic blood pressure | type of device |
| Random_BS | Random blood sugar | finger stick, device |
| HTN_Medication | Does patient take medication for hypertension? | 0 = No, 1=Yes |
| DM_Medication | Does patient take medication for diabetes mellitus? | 0 = No, 1=Yes |
| HIV_Medication | Does patient take medication for HIV? | 0 = No, 1=Yes |
| DM_Medication | Does patient take medication for diabetes mellitus? | 0 = No, 1=Yes |
| No_Medication | Does patient take no medications? | 0 = No, 1=Yes |
| Current_Smoker | Is patient a current cigarette smoker? | 0 = No, 1=Yes, nc = not collected |
| Current_Alcohol | Is patient a current drinker of alcohol? | 0 = No, 1=Yes, nc = not collected |
| Occupation | What is patient's current occupation? | 0-10, nc = not collected |
| Image_Include? | Should image be included or not included based on quality assessment? | 0 = No, 1=Yes, nc = not collected |
| Img_Quality_Issues? | Does image have quality issues? | 0 = No, 1=Yes |
| IMG_Poor_Focus? | Does the image have poor focus? | 0 = No, 1=Yes, -99 = intentionally not collected |
| IMG_Too_Dark_or_Bright? | Does the image have illumination defects? | 0 = No, 1=Yes, 99 = intentionally not collected |
| IMG_Artifacts? | Does the image have artifacts? | 0 = No, 1=Yes, 99 = intentionally not collected |
| IMG_ICDR_Score | International Classification of Diabetic Retinopathy Score | 0 = no DR, 1 = micros only, 2 = microaneurysm, cws, or retinal hemorrhages (<25/quadrant), 3 = >25 hemes per quadrant, 4 = proliferative diabetic retinopathy, 5 = unable to determine |
| IMG_Macular_Edema | Presence of Diabetic Macular Edema | 0 = no DME, 1 = DME, 2 = unable to determine, 99 = intentionally not collected |
| C_D_Ratio | Cup-to-disc ratio | 1 = 0.0-0.6, 2 = 0.7-0.8, 3 = 0.9-1.0, -99 intentionally not collected |
| Optic_Nerve_Normal? | Is optic nerve normal? | 0 = No, 1=Yes, -88 = not collected, -99 = intentionally not collected |
| Quick_Qual_Metric | Image quality metric from Quick Qual, probability of the image being "bad" | link to QQ, 99 = intentionally not collected (image defect) |
| Fractal_Dimension_Metric | Fractal dimension metric from DART | link to DART, 100 = intentionally not collected (image defect) |
This represents all 38 variables from the MoDRIA dataset. The complete dataset includes variables for retinal imaging, clinical assessments, laboratory results, diabetes management, and research protocols. All variables are designed to support comprehensive ophthalmological research and clinical care, with particular focus on diabetic retinopathy screening and management in African populations.