Role of Artificial Intelligence (AI) In Eye Care

Introduction

Artificial intelligence is a new, innovative technology that mimics human intelligence to perform tasks and give better results. They can learn, understand and solve problems just like humans. Artificial intelligence is fully digitalized, works more efficiently, and saves time. AI has now been introduced into the health sector for accurate diagnosis of various health conditions.

How Artificial Intelligence Works on Eye Care?

Artificial intelligence refers to a term that accomplishes a task by computer application with minimal human participation. AI in ophthalmology mainly works on diseases with a high rate of incidence. The diseases include

• Diabetic retinopathy.

• Age-related macular degeneration.

• Glaucoma.

• Retinopathy of prematurity.

• Age-related or congenital cataracts.

• Retinal vein occlusion.

What Is the Importance of AI in Eye Care?

The introduction of AI in the healthcare sector is intended to improve accuracy and decision-making. This improves the diagnosis, reduces error, and saves time and early management of diseased conditions.

How Does AI-Based Technology Help Eye Care?

The AI-based systems are deep learning computers, where they are learned and trained with various pictures of eyes, which includes normal eyes and eyes in various diseased conditions. Algorithms were made to make the AI system understand normal and abnormal images and also identify the differences. AI-based technology uses images of the retina to determine the cardiovascular disease risk in patients. X-ray images of the retina were used to find out about pediatric pneumonia.

Following are various disease conditions where artificial intelligence is used.

• Diabetic Retinopathy: The IDx-DR is the first FDA-approved artificial intelligence-based technology to diagnose diabetic retinopathy. This technological device analyzes images of the eye that are taken with a retinal camera. It helps with the proper diagnosis and suggests whether to consult an ophthalmologist or not. The AI-based software can accurately detect the early warning signs of diabetic retinopathy.

• Macular Degeneration: Macular degeneration affects central vision, and the initial symptoms are not noticeable until the vision is blurry. The AI-based software has made the identification of early signs of macular degeneration. Images selected by the experts were used to teach the AI software. This helps in providing diagnostic examples and helps produce the results faster and better. The software allows the ophthalmologists to measure and map the disease's progression. Computer systems rapidly analyze multiple eye scans and other information. This AI software helps make treatment decisions for macular degeneration.

• Cataract: Cataract is a common eye disease of old age that causes clouding of the lens and blindness. The AI technology also helped in the diagnosis of pediatric cataracts.

• Retinopathy of Prematurity: Retinopathy of prematurity is a leading cause of childhood blindness all over the world. This can be prevented by early diagnosis and treatment. The increased number of screenings and follow up to be carried out for retinopathy of prematurity consume a lot of time and energy. The application of AI has improved the efficacy in the treatment of retinopathy of prematurity. Patients with retinopathy of prematurity plus disease or retinopathy in zone one stage three, even without plus eye disease, can be treated with a timely diagnosis. The AI technology helped in the timely diagnosis of retinopathy of prematurity, which was otherwise difficult to diagnose.

• Glaucoma and AI: Glaucoma affects the optic nerve of the eyes, which sends signals to the brain, where the image is processed. The irreversible damage to the optic nerve cannot be prevented, but the progression can be delayed. So the early diagnosis of glaucoma is important. The factors that can be assessed for the glaucoma diagnosis are,

1. Intraocular pressure.

2. The thickness of retinal nerve fiber.

3. Optic nerve and visual field examination.

AI and machine learning helped in the early diagnosis by assessing factors like,

1. Cup disc ratio.

2. Fundus image.

3. Visual field.

4. The thickness of retinal nerve fiber.

The accuracy has increased, which has helped in the proper treatment and delayed the disease progression.

• Retinal Vein Occlusion: It is the second-most-ranked cause of blindness after diabetic retinopathy. The major cause of retinal vein occlusion is the sclerotic retinal artery compressing the retinal vein and blocking the blood return. Machine learning is rarely used in retinal vein occlusion. But the high accuracy in diagnosis helped in timely treatment and recovery.

What Are the Limitations of Article Intelligence in Eye Care?

• The formation of an algorithm requires huge costs and training experience.

• AI is useful for diseases with high morbidity, and diagnosing rare diseases is difficult with AI technology.

• AI technology cannot be completely benefited in the identification of a disease separated from the algorithm.

• AI technology recognizes a disease's condition fully mechanically, and a small variation of the feature from the usual features of the disease cannot be recognized and might affect the diagnosis.

• A small, unusual variation of the disease may be missed.

• AI technology can diagnose the majority of diseases, but not all of them.

• The characteristics of diseases and algorithm parameters differ for each patient case.

What Is the Future of Eye Care?

The benefits of AI include predictive analysis for certain diseases. It can help in determining whether a patient can have a positive or negative impact from certain eye treatments. These predictions are based on the patient’s past medical history, images, and genetics. Advanced imaging technologies have made the diagnosis easier and time-saving.

What Are the Challenges to Artificial Intelligence in Eye Care?

Practical Challenges:

• AI technology cannot be solely utilized for the diagnosis of eye diseases, it requires the clinical skills of a doctor to validate the diagnosis.

• AI systems are designed for the detection of a particular disease and switching between the AI systems for each possible disease is difficult.

Ethical Challenges:

• The ethical and legal issues arising from AI-based systems to classify clinical data cannot be explained.

Technical Challenges:

• Another challenge is the lack of quality and validation. The process is time-consuming and requires trained professionals.

Lack of Resources:

• Implementation of digital ways of diagnosing is difficult in areas where there is no access to the internet.

Conclusion

Artificial intelligence has been used in ophthalmology for the early diagnosis of eye diseases and also for rare eye diseases. The innovation in other technologies has made screening, diagnosis, and treatment faster and could eliminate blindness. AI could save time and reduce manpower. The accuracy of the AI-based diagnosis depends on the quality of the data on which it is trained.