Bioinformatics is one of the leading applications of AI. It works wonders in promoting access to timely and affordable healthcare, while simultaneously automating the process and reducing the burden of doctors. There is much potential in Medical imaging identification, especially in diagnosing blindness causing diseases in Ophthalmology.
Diabetic Retinopathy:
Diabetic Retinopathy mainly affects the working age population, impacting their retinal microvasculature, leading to progressive blindness.
Detection at early stages is crucial in establishing medical protocol. The Deep Learning models show high sensitivity and specificity with an AUC score of 0.99 in automated predicting of DR.
Age-related Macular Regeneration:
AMD is one of the leading causes of central vision loss in people of age > 50. This disease requires regular screening.
The AI program is fed images of healthy eyes and images of eyes affected by early, intermediate, and late-stage AMD to identify disease onset.
The sensitivity ranges from 87% to 100%, ensuring high diagnostic accuracy.
Retinal vein occlusion:
RVO stands second to Diabetic Retinopathy in causing blindness.
It presents itself in the geriatric demographic with existing vascular sclerosis, like hypertension, arteriosclerosis, and cardiovascular disease.
AI has achieved a 97% accuracy in the automatic detection of RVO from Fundus images.
Retinopathy of prematurity :
ROP presents itself in Children and requires timely treatment and regular screening in infants, especially those with Plus Disease.
The current AI can detect Plus Disease with an accuracy of 95%, easily comparable with Expert diagnosis.
Anterior segment diseases :
Anterior Segment diseases like Glaucoma and Cataracts are the most common ailments to affect the eyes. With high-resolution fundus images, AI offers early diagnosis with accuracy ranging from 63.7% to 93.1%.
The referable diagnosis can delay the progression of Glaucoma. It has also proved invaluable in the detection of Paediatric cataracts.
Artificial Intelligence offers accuracy in detecting ophthalmological diseases that is on par with Medical experts. It is essential for timely diagnosis and intervention to reverse and arrest the progress of the diseases.