The College of Science and Engineering (CSE) at Hamad Bin Khalifa University (HBKU) proposed a novel artificial intelligence (AI)-enabled system that can be used to diagnose cardiovascular diseases (CVD) based on retinal (innermost, thin, light sensitive layer of eye) images and bone health data.
This seminal method was developed in-house at CSE, funded by Qatar Biobank. The research findings were published in the Open Access Sensors journal which is publicly available for the community.
Learning-based diagnoses
CSE’s proposed, deep learning-based system diagnoses CVD using combined information from two imaging techniques. The first, retinal images provides medical practitioners with a fast way to examine optic nerves and diagnose diseases such as hypertension, diabetic retinopathy or arteriosclerosis. The second is dual-energy X-ray absorptiometry (DXA) data on bone mineral density. DXA is a form of non-invasive X-ray technology used to analyse bone health and bone loss. DXA has been approved recently for use in CVD diagnosis by the US Food and Drug Administration (FDA).
The investigators leveraged deep learning techniques to fuse the two sets of information (retinal imaging plus DXA data) to diagnose the onset of CVD in a sample group of Qatari adults. Based on the experiment results, the proposed novel method demonstrates major potential advancement for the early detection of CVD in a fast, non-invasive, and low-cost manner.
In Qatar, 69% of mortalities are caused by chronic diseases where 24% are caused by CVD. The CSE research is in line with Qatar National Health Strategy 2018-2022 which aims to reduce mortality caused by diseases such as CVD by 15%. One way of achieving this is by utilising precision or personalised health for its early detection and diagnosis.
Dr Tanvir Alam, CSE Assistant Professor (Information and Computing Technology Division) and the project’s lead principal investigator, said that at the heart of the study is the state-of-the-art AI techniques to prevent, diagnose and treat cardiovascular disease. He said that the results demonstrate how the use of AI in healthcare, especially in deep learning-based models, is evolving, its potential to be expanded in clinical settings, exciting.
The study has been enriched by the joint efforts of research collaborators and experiments by HBKU PhD student Hamada Al-Absi and the cooperation Qatar Biobank.
HBKU CSE Founding Dean Dr Mounir Hamdi said that this kind of seminal, innovative research conducted at CSE, will enable precision medicine solutions for Qatari society and elevate Qatar’s leadership in the field of AI and personalised healthcare.
For more information on CSE and their work, visit cse.hbku.edu.qa.
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