Aain-D: AI for diabetic retinopathy from retinal images
AI that grades diabetic retinopathy from retinal photographs, including a new Radon-transform method released as open-source software.
Part of DAQIQ · دقيق: Diabetes Analytics for Quality of life through Individualised care in Qatar.
- Status
- Completed
- Duration
- 2022–2025
- Funding
- HBKU Innovation Centre (USD 50,000)
- Team
- 3 people
- Publications
- 5
In plain language
We built AI that looks at photographs of the back of the eye and grades how far diabetic eye disease has progressed, so screening can reach more people.
Why it matters
- Diabetic retinopathy is a leading cause of preventable blindness, and early treatment works.
- Screening depends on specialists reading many images. AI can help prioritise the people who need care first.
Overview
Diabetic retinopathy is a leading cause of preventable vision loss, and early detection depends on regular retinal screening. Aain-D, funded by the HBKU Innovation Centre, set out to build an AI-enabled mobile tool to help diagnose diabetes and stratify diabetic retinopathy from retinal images.
The research introduced RadEx, a nonlinear Radon-inspired transform that turns retinal images into sinograms, and RadFuse, which combines these sinograms with fundus images in deep networks. Tested on the public APTOS-2019 and DDR datasets, the method achieved high grading accuracy and was released as open-source software.
Objectives
- Grade diabetic retinopathy severity from retinal fundus images.
- Develop new image-feature methods based on the Radon transform.
- Evaluate AI models for generating synthetic retinal images.
- Release the methods as reusable open-source software.
Approach and data
- Deep learning on retinal fundus images.
- Linear and nonlinear Radon-transform sinograms as an additional input.
- Comparative evaluation of generative models for retinal image synthesis.
Key results
- Quadratic kappa for five-class retinopathy grading with RadFuse, with 87% accuracy
- 93.2%
- Open-source Python package releasing the nonlinear Radon transform
- RadEx
Team
Publications 5
-
Integrating non-linear Radon transformation for diabetic retinopathy grading
Mohsen F, Belhaouari S, Shah Z
Scientific Reports 15: 30706, 2025
Journal articleAain-D: AI for diabetic retinopathy from retinal imagesHarnessing the power of artificial intelligence: predicting type 2 diabetes onsetDr. Farida MohsenDr. Zubair Shah
-
RadEx: an open source Python package for nonlinear Radon transformation
Mohsen F, Islam A, Mohsen F, Shah Z, Belhaouari SB
Software Impacts: 100779, 2025
Journal articleAain-D: AI for diabetic retinopathy from retinal imagesHarnessing the power of artificial intelligence: predicting type 2 diabetes onsetDr. Farida MohsenDr. Zubair Shah
-
A comparative evaluation of artificial intelligence-based models in retinal image synthesis
Mohsen F, Aldhubri A, Biswas MR, Shah Z
2nd International Conference on Foundation and Large Language Models (FLLM 2024): 51–57, 2024
Conference paperTalk and slidesAain-D: AI for diabetic retinopathy from retinal imagesDr. Farida MohsenDr. Md Rafiul BiswasDr. Zubair Shah
-
Introducing RadEx: adaptive parameterized feature extraction from medical images
Islam A, Mohsen F, Shah Z, Belhaouari SB
Computer Graphics International Conference (CGI 2024): 278–294, Springer, 2024
Conference paperTalk and slidesAain-D: AI for diabetic retinopathy from retinal imagesHarnessing the power of artificial intelligence: predicting type 2 diabetes onsetDr. Farida MohsenDr. Zubair Shah
-
Multimodal deep learning for diabetic retinopathy grading: integrating linear-Radon sinograms and retinal fundus images
Mohsen F, Shah U, Islam A, Shah Z, Belhaouari SB
IEEE 26th International Workshop on Multimedia Signal Processing (MMSP 2024): 1–5, 2024
Conference paperTalk and slidesAain-D: AI for diabetic retinopathy from retinal imagesHarnessing the power of artificial intelligence: predicting type 2 diabetes onsetDr. Farida MohsenDr. Zubair Shah
Talks and media
Software and datasets
- RadEx, open-source Python package for nonlinear Radon transformation
Get involved
Use or extend our methods
RadEx is open-source. Contact us to collaborate on retinal AI or to test the methods on new data.