Harnessing the power of artificial intelligence: predicting type 2 diabetes onset
PhD research showing that combining retinal images, ECG features and clinical risk factors improves prediction of type 2 diabetes in Qatar, and introducing new open-source image methods.
Part of DAQIQ · دقيق: Diabetes Analytics for Quality of life through Individualised care in Qatar.
- Status
- Completed
- Duration
- 2021–2024
- Funding
- PhD research, College of Science and Engineering, HBKU
- Team
- 4 people
- Publications
- 8
In plain language
This research taught AI to combine eye photographs, heart-tracing (ECG) data and routine clinical information to better predict who will develop type 2 diabetes.
Why it matters
- Standard risk scores use a few clinical factors and miss many people at risk.
- Eye and heart measurements carry early signs of changes in blood vessels that come with diabetes.
Overview
This doctoral research began by mapping the field: scoping reviews of AI for diabetes risk prediction and of methods that fuse health records with medical images.
It then built multimodal deep-learning models on longitudinal Qatar Biobank cohorts. ECG-DiaNet combined ECG-derived features with clinical risk factors and improved prediction over standard risk scores. The research also introduced RadEx, a nonlinear Radon-based image transform that captures subtle retinal patterns, released as open-source software, and used it to grade diabetic retinopathy.
Research question
Does combining retinal images, ECG features and clinical risk factors improve prediction of type 2 diabetes onset in a Middle Eastern population?
Objectives
- Review AI methods for diabetes risk prediction and for fusing health records with imaging.
- Develop multimodal deep-learning models for incident type 2 diabetes in Qatar Biobank cohorts.
- Create new image-feature methods for retinal images.
- Release methods as reusable open-source software.
Approach and data
- Scoping and systematic reviews to set the research agenda.
- Multimodal fusion of retinal fundus images, ECG-derived features and clinical data.
- Nonlinear Radon transformation (RadEx) and sinogram fusion (RadFuse) for retinal analysis.
Key results
- AUROC for ECG-DiaNet, against 0.822 for standard risk scores (p < 0.001) (Scientific Reports, 2025)
- 0.845
- Quadratic kappa for five-class diabetic retinopathy grading with RadFuse
- 93.2%
- Open-source Python package for nonlinear Radon transformation
- RadEx
Timeline
- 2022–2023Reviews of AI for diabetes prediction and of EHR–imaging fusion (npj Digital Medicine).
- 2024RadEx and retinopathy grading papers; PhD completed in December.
- 2025ECG-DiaNet and RadEx software papers published.
Team
Dr. Zubair Shah
PhD supervisor
Dr. Hazrat Ali
Co-author
Lecturer in Artificial Intelligence and Data Science, University of Stirling, UK
Dr. Md Rafiul Biswas
Co-author
Postdoctoral Researcher, College of Humanities and Social Sciences, HBKU
Partners
- Qatar Biobank (data access)
Publications 8
-
ECG features improve multimodal deep learning prediction of incident T2DM in a Middle Eastern cohort
Mohsen F, Safa A, Shah Z
Scientific Reports 15: 27164, 2025
Journal articleHarnessing the power of artificial intelligence: predicting type 2 diabetes onsetDr. Farida MohsenDr. Zubair Shah
-
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
-
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
-
A scoping review of artificial intelligence-based methods for diabetes risk prediction
Mohsen F, Al-Absi HRH, Yousri NA, El Hajj N, Shah Z
npj Digital Medicine 6: 197, 2023
Journal articleHarnessing the power of artificial intelligence: predicting type 2 diabetes onsetDr. Farida MohsenDr. Zubair Shah
-
Artificial intelligence-based methods for fusion of electronic health records and imaging data
Mohsen F, Ali H, El Hajj N, Shah Z
Scientific Reports 12: 17981, 2022
Journal articleHarnessing the power of artificial intelligence: predicting type 2 diabetes onsetDr. Farida MohsenDr. Hazrat AliDr. Zubair Shah
-
Customized and automated machine learning-based models for diabetes type 2 classification
Mohsen F, Biswas MR, Ali H, Alam T, Househ M, Shah Z
Advances in Informatics, Management and Technology in Healthcare: 517–520, IOS Press, 2022
Short paperHarnessing the power of artificial intelligence: predicting type 2 diabetes onsetDr. Farida MohsenDr. Md Rafiul BiswasDr. Hazrat AliDr. Zubair Shah
Software and datasets
- RadEx, open-source Python package for nonlinear Radon transformation
Ethics
Analysis of de-identified Qatar Biobank data under an approved data-access project.
Get involved
Use RadEx or collaborate
RadEx is open-source. Contact us to collaborate on multimodal diabetes prediction.