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Projects
Studies that make up DAQIQ, past and present: how people live with diabetes in Qatar, and the analytics and AI that make care individualised.
- 6
- Research projects
- 2
- Active now
- 15
- Project publications
- 4
- Partner organisations
- 4
- Funding sources
Living with diabetes
Living with Diabetes in Qatar: the LDQ Cohort
We will ask the same adults with type 2 diabetes in Qatar, once a year for five years, where they get advice about their diabetes, from doctors to AI chatbots, and how that advice affects their wellbeing and daily care.
StatusActive · Preparatory year
Duration2026–2032
LeadDr. Zubair Shah
SupportHBKU institutional resources
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Research themes
Five questions, one purpose
Each project answers part of one question: how can care for diabetes in Qatar fit each person better? Choose a theme to filter the projects below.
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Living with diabetes
LDQ Cohort
Living with Diabetes in Qatar: the LDQ Cohort
A five-year cohort following adults with type 2 diabetes across four care settings, to learn how digital and AI advice shapes diabetes distress and self-management.
Project leadDr. Zubair Shah
Connected and proactive care
Proactive Care
AI-driven healthcare surveillance and proactive care recommendation in smart environments
AI that brings together data from connected health devices and clinical knowledge to spot changes early and recommend timely, personalised care for people living with diabetes.
Project leadDr. Zubair Shah
1 paper
Risk prediction
Longitudinal Risk
Longitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
PhD research that fused bone scans, retinal images, ECG, genetic, laboratory and lifestyle data from Qatar Biobank to predict who will develop type 2 diabetes, and to study its complications.
Project leadDr. Sulaiman Khan
5 papers
Retinal and medical imaging
Aain-D
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.
Project leadDr. Zubair Shah
5 papers
Risk prediction
T2D Onset
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.
Project leadDr. Farida Mohsen
8 papers
Community and peer support
C.P.-Support
C.P.-Support: peer support for people living with diabetes
An intelligent peer-support platform connecting people who manage chronic conditions such as diabetes, so they can learn from each other’s experience.
Project leadDr. Zubair Shah
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Our research over timeFrom the first peer-support prototype in 2020 to the five-year LDQ Cohort.
Work with us
Have data, a clinic or an idea?
We partner with clinicians, health organisations and researchers on studies that improve life with diabetes in Qatar.