Advanced Machine Intelligence Methods for Identifying Diabetic Eye Conditions

We are living in invigorating times where medical science is on an evolutionary trajectory. In recent years, machine learning and artificial intelligence have brought a paradigm shift in the healthcare industry. The development of intelligent algorithms has made it possible to visualize data and gain new insights useful for patients and clinicians. As per a survey conducted in 2017, the International Diabetes Federation (IDF) stated that the two most populous countries in Asia, India, and China have proportionally high diabetic populations [1] [2]. According to Vision Atlas, India and China together account for 45 % of visual impairment globally. Diabetic patients are 25 times more vulnerable to the possibility of losing vision. The findings from the 10th Edition of the IDF Diabetes Atlas, which were published on December 6th 2019, report that over 74 million adults in India are living with diabetes mellitus [3] [4]. Figure 1 shows the performance of India globally and in the Southeast Asian region concerning the diabetic population. One in eight persons above 50 years in India is a diabetic; one in every 46 diabetics is blind; and one in seven has some form of impairment in their vision due to high blood sugar levels, according to a diabetes and diabetic retinopathy survey by the All India Institute of Medical Sciences (AIIMS) in collaboration with Ministry of Health and family Welfare carried out between 2015 to 2019 [191]. India has the highest number of visually impaired. It ranks fifth when it comes to an overall increase in the rate of vision impairment as per the International Agency for the Prevention of Blindness (IAPB), as shown in Figure 2 [5]. Hence, for prevention of vision loss of diabetic population; routine eye check-up is recommended for early diagnosis of any eyesight threatening complication. Figure 1shows the global diabetic population based on the data available on International Diabetic Federation. Figure 2 gives the global visually impaired population based on data from Vision Atlas.