Role of AI in Early Detection of Critical Health Issues Related to Diabetes

AI in the early detection of critical health issues related to diabetes, such as retinopathy, is a tremendous development in the healthcare industry.

Science and technology have been witnessing rapid development in the healthcare sector, however, diabetes remains one of the incurable lifelong chronic illnesses. Researchers and doctors are focusing more to improve self-management skills to help patients enhance their metabolic control and quality of life. To add valuable knowledge and boost R&D, artificial intelligence technologies have also been contributing their part in helping to detect serious health-related issues that are caused by diabetes. DIAGNOS AI  (TSX.V-ADK) technology's prime focus lies around these lines. It gives access to a low-cost pre-screening test to monitor clinical health issues related to diabetes such as retinopathy, hypertension, and cardiovascular diseases.

DIAGNOS is an expert in the early detection of health problems using its FLAIRE artificial intelligence (AI) platform. The FLAIRE platform is used in applications such as Computer Assisted Retina Analysis (CARA). CARA's artificial intelligence-based image enhancement algorithms can provide clearer, sharper retinal images that can be used for analyzing anomalies. The good news is that Diagnosis has opened AI screening clinics at Magrabi Hospitals in Saudi Arabia as well as a pilot with the largest ophthalmological hospital in Sinaloa State, Mexico.

According to the World Health Organization (WHO), it is estimated that nearly 422 million people across the globe are suffering from diabetes and this number is likely to increase to 642 million by 2040. Thanks to AI in early detection of diabetes-related health issues through an automated process that is cheaper, faster, and more precise than manual exams. The application of AI in medical science is becoming comprehensive yielding many benefits. The key advantages include low cost, efficiency, broad coverage, portability, diversity, and service-end productivity.

Diabetic retinopathy is a disease that causes lesions on the retina having adverse effects on the vision. And this condition is common among most diabetic patients without even displaying any kind of symptoms in the initial stages. But AI and deep learning can help in mitigating some of the challenges by detecting diabetic retinopathy during the early stages itself. This is made possible using color fundus photographs or CFP where deep learning is used to access the raw CFPs to produce a target outcome prediction. With the help of DIAGNOS' AI telemedicine solutions, patients can detect diabetic retinopathy at an earlier stage.  (www.diagnos.ca)

Eye testing has become even more sophisticated with the help of AI, with regard to Optometry and Ophthalmology, AI has been predominantly aimed at diseases with high prevalence such as diabetic retinopathy. As diabetes is the largest cause of vision loss in the world, screening of the retina contributes to the early detection of diabetic retinopathy and helps in timely treatment.

And AI also is capable of identifying the risk factors and phenotypes when it comes to hypertension too. It can predict the risk of hypertension and help in identifying the factors that are associated with it. AI can also be promising in the furthering precision of medicine related to cardiovascular diseases including hypertension.

While coming to cardiovascular diseases, which are also commonly seen among diabetes patients can also be detected at the early stages with the help of AI-enabled retinal screening. As the retina is the only part of our body where doctors can easily observe the condition of arteries and veins without any invasive procedures, early detection of atherosclerosis in the retinas of diabetes patients can signal a red flag in the coronary arteries too. This can thereby help in reducing cardiovascular diseases among diabetic patients.

Even though AI adoption is in its budding phase in retinal technologies, with the rising demand for accuracy, patients' safety, and efficiency the AI technologies are expected to boost the adoption of AI-enabled medical imaging in the near future.