About
Farida Mohsen’s doctoral research showed that combining retinal fundus images, ECG-derived features and clinical risk factors improves type 2 diabetes risk prediction in longitudinal Qatar Biobank cohorts. She developed ECG-DiaNet, a multimodal deep learning model, and RadEx, a nonlinear image transform released as open-source software.
Research focus: Multimodal deep learning for diabetes risk prediction and diabetic retinopathy grading
Thesis
Harnessing the Power of Artificial Intelligence: Predicting Type 2 Diabetes Onset
Projects
Talks
Publications with DAQIQ 10
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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 onset
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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 onset
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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 onset
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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 images
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Genetic biomarkers and machine learning techniques for predicting diabetes: systematic review
Khan S, Mohsen F, Shah Z
Artificial Intelligence Review 58(2): 41, 2024
Journal articleLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
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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 onset
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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 onset
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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 onset
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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 onset
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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 onset