Around 537 million individuals between the ages of 20 and 79 are affected by diabetes. It is
predicted that the total number of diabetic cases will increase to 643 million by 2030. Because diabetes is a chronic disease, patients with diabetes suffer from this disease for a long time.
The disease is associated with different complications for patients, their families and public
health systems by reducing both the quality of life and life expectancy and causing different
potentially life-threatening problems.
Despite advances in medical technology and screening programs, gaps in the detection of type 2 diabetes within the general population persist, leading to delayed diagnosis and the next complication of this condition. Therefore, there is an urgent need to develop software using artificial intelligence-based digital analysis, integrating traditional and adapted national risk factors for risk prediction and identification of cases at high risk.
Artificial intelligence (AI) has surfaced as a promising tool to enhance early detection and
screening efficacy. Moreover, early detection is crucial for successful treatment and improving overall outcomes. Identified challenges including limited awareness & education, lack of routine health checkups, gaps in detection, false-negative results, age and gender disparities underscore the need for proactive solutions. The integration of AI in the detection of people at high risk of type 2 diabetes holds great promise to address these challenges and improve outcomes. As technology continues to evolve, digital health platforms and AI algorithms will become even more sophisticated, accurate, and scalable for earlier detection and improved management of type 2 diabetes, finally improving outcomes and reducing the burden of disease. We hypothesize that our model will streamline the decision-making processes, aid in identifying high-risk cases, reduce healthcare utilization, and minimize future treatment costs. Therefore, the primary aim of this study is to develop and validate an AI-based software incorporating both traditional and adapted national risk factors to effectively predict and identify individuals at high risk of type 2 diabetes. Currently, our AI team is at the forefront of developing diverse data mining approaches and risk prediction models. It was recently pioneered for the application of different AI methods and developing the first generation of AI models for breast and colorectal cancers, as well as contributing to the Big Prostate Cancer Consortium in Australia. This software serves as the foundation for the current project, reflecting our commitment to advancing cutting-edge technologies for improved diagnostics and cost-effective, patient-centric healthcare solutions.
Keywords: Diabetes, artificial Intelligence, digital Health, Early Detection, Machine Learning.
Objectives
1. Develop and validate a comprehensive approach based on AI to identify individuals
who are at risk of developing T2D.
2. Develop a national risk factor assessment tool for early prediction of T2D.
3. Provide an algorithm and explore the potential of adapted-based national risk factors/
cut-off of T2D risk factors.
4. Provide a national guideline based on adapted national risk factors/cut-off of T2D risk
factors.
5. Develop and validate a national Oman software to provide services to patients with
T2D using digital health and artificial intelligence in order to detect people who are at
risk.
Funding Agency:
MOHERI
Collaborative Partners:
Collaborative Partners:
Collaborator 1 Within Oman Oman Medicine Health services collaboration
Collaborator 2 Within Oman, Oman Medicine Health services collaboration
Collaborator 3 Abroad Australia Biomedical Sciences
Research collaboration
Collaborator 4 Abroad Iran, Islamic Republic Of
Medical Sciences Research collaboration
Collaborator 5 Abroad Australia Medicine Research collaboration
Collaborator 6 Abroad New Zealand Medicine Research collaboration
Collaborator 7 Abroad United States of America
Medicine Research collaboration