This case study focuses on AI-powered Diabetic Retinopathy (DR) detection and the importance of accurate medical imaging in improving diagnostic outcomes. High-speed, expert-validated annotation plays a critical role by providing the precise data needed for reliable scan analysis and early DR identification. The data annotation capabilities ensure the AI model is trained with high-quality information, leading to more accurate and efficient diagnosis.
Client
This project was a collaboration between a leading medical R&D organization and MulticoreWare to develop an AI-driven solution for early detection and diagnosis of Diabetic Retinopathy (DR) through annotated medical imaging. The initiative aimed to enhance diagnostic accuracy and accessibility, particularly in regions with limited access to ophthalmologists and heavy work loads.
Challenge
Diabetic Retinopathy is a leading cause of vision impairment among diabetic patients, requiring early detection to prevent severe complications. Traditional diagnosis relies on manual examination of retinal fundus images, a process that is time-consuming and subject to variability among clinicians. In underserved areas, delayed diagnoses often result in deteriorating patient conditions. There was a critical need for an automated, accurate, and scalable AI solution that could enhance diagnostic efficiency while ensuring consistency across different medical settings.
Solution
MulticoreWare developed a robust machine learning pipeline for processing annotated datasets of retinal fundus images. Images were labelled for pathological features like microaneurysms, hemorrhages, exudates, cotton wool spots and neovascularization using polygon annotations and color-coded layers to differentiate healthy from abnormal regions. Below Annotation guidelines were designed by MulticoreWare and effectively implemented during the project.
Image Level Annotation:
Adopt a standard grading system for diabetic retinopathy, such as the International Clinical Diabetic Retinopathy (ICDR) scale:
- No DR: No abnormalities detected.
- Mild DR: Microaneurysms only.
- Moderate DR: More than microaneurysms but less severe than proliferative DR.
- Severe DR: Intraretinal hemorrhages, microaneurysms in all quadrants, and possible venous beading.
- Proliferative DR: Neovascularization or vitreous/preretinal hemorrhages.
| Pathological Feature | Description | Annotation Guideline |
|---|---|---|
|
Microaneurysms |
Tiny red dots (localized outpouching of capillaries). |
Mark as points or small circular regions. |
|
Hemorrhages |
Dark red spots are caused by blood leakage. |
Annotate regions with irregular shapes and bleeding appearance. |
|
Exudates |
Yellow-white spots (lipid deposits). |
Outline bright areas, especially near the macula. |
|
Cotton Wool Spots |
White patches are caused by nerve fiber damage. |
Mark white fluffy areas as irregular polygons. |
|
Neovascularization |
New abnormal blood vessels. |
Use lines to annotate branching blood vessels. |
|
Macular Edema |
Swelling in or near the macula. |
Annotate swollen areas near the fovea, if visible. |
|
Vitreous Hemorrhage |
Blood in the vitreous cavity. |
Annotate large, darkened areas covering the retina. |
|
Optic Disc |
A round, yellowish part of the retina at the back of the eye |
Annotate a round yellowish area where the optic nerve and retina meet |
Object Level Annotation:
Annotated Image of Retinal Fundus
Technology used
Custom Annotation Tool
- We utilized our in-house custom annotation tool to annotate retinal images efficiently.
- Our tool is designed to handle high-precision medical image annotations of any type, ensuring accuracy, security and consistency.
Annotation Methodology
- We employed Semantic Segmentation to label different regions of the retinal images. This method allowed us to precisely segment and classify various structures within the images, facilitating detailed analysis and model training.
- By leveraging our custom annotation tool, we ensured streamlined workflows and maintained high-quality annotations for optimal model performance.
Business Impact
- MulticoreWare’s AI-driven annotation transformed Diabetic Retinopathy screening, making it faster, more precise, and scalable.
- Automated analysis reduced diagnosis time, while AI-based classification improved accuracy and minimized human errors.
- The system’s ability to handle large volumes of data made it particularly effective in resource-limited healthcare environments, allowing for broader adoption of AI-powered screening in clinical practice.
Conclusion
Through advanced medical image annotation and AI-driven diagnostics, MulticoreWare successfully enhanced Diabetic Retinopathy detection in collaboration with the medical R&D organization. Our expertise in AI-powered healthcare solutions continues to drive innovation and accessibility in medical imaging.
Discover how we can help you bring AI innovation to healthcare by contacting us at info@multicorewareinc.com
