According to Financial Express, the number of people living with diabetes mellitus is predicted to reach 700 million by 2045, with diabetic retinopathy being identified as a potential global health catastrophe. According to some studies, diabetic retinopathy is the primary cause of blindness in people worldwide.
According to research, over one-third of people with diabetes will develop diabetic retinopathy. Maintaining a healthy lifestyle and having frequent eye exams can aid in diagnosing and preventing Diabetic Retinopathy, which can result in irreversible vision loss if left untreated.
Convolutional neural networks (ConvNets), a kind of algorithm, are used in most AI systems.ConvNets are based on the idea that a computer can learn from a large quantity of data, such as ophthalmic pictures, provided by a programmer. The computer then makes its best estimate (although at random) as to what’s wrong with each image displayed.
When an algorithm provides an inaccurate forecast, it notifies the user and appropriately modifies the algorithm’s settings. It learns from its triumphs and mistakes, remembers the input it receives, and adjusts its behavior to maximize the possibility that its next diagnosis prediction will also be accurate.
This procedure is repeated until the algorithm achieves the required degree of accuracy or the maximum number of iterations is reached. This method of operation, which we term data-driven machine learning, is depicted in Figure 1.
As reported, the more data exposed to the algorithm during training, the better it will perform during testing. Soon, AI is likely to play a substantial role in several aspects of ocular disease care, including screening, diagnosis, and therapy.
AI will play a key role in ocular illness screening in the future. If a patient sees a change in their vision on an Amsler grid, they may only sometimes require immediate attention from a retina expert. New artificial intelligence (AI) tools make it possible to take a retinal scan and quickly review it to decide when a patient should next see a retina expert. As medical imaging advances, artificial intelligence systems will evaluate patient pictures, with human verification following later.
AI may assist the retina expert in organizing the clinic by triaging patients, offering a viable diagnosis, allocating rooms, and predicting whether a patient would require additional procedures or referrals. Because of the retina specialist’s expertise and experience, more accurate and efficient diagnosis and treatment will be feasible. This type of intelligent technology has the potential to enhance patient outcomes, reduce wait times, and maximize resource utilization.
However, there is now a need for more experts, such as ophthalmologists, in the United States. According to a 2017 Association of American Medical Colleges prediction, the United States would be short roughly 60,000 specialty doctors by 2030.
When the growing number of older adults in the United States is evaluated, the severity of the present physician shortage becomes clear. The application of AI has the potential to solve this issue and ensure that individuals always have access to the medical treatment they require.
Due to factors such as sickness severity and patient geography, doctors are not always required, and it is sometimes not even possible, to be physically present with their patients at the precise moment they need them. Instead, an ophthalmic technician may take a snapshot that is then evaluated by an AI system.