Manish Panjiyar, M.Optom

The Sankara Nethralaya Academy, A unit of Medical Research Foundation in collaboration with The Tamil Nadu Dr. M.G.R Medical University, Chennai.



For prevention of vision loss and promotion of quality of life, it is crucial to detect and diagnose eye diseases at an early stage so that an appropriate management strategy can be made. Integration of Artificial Intelligence (AI) in Ophthalmology has a great potential to create a revolutionary change in the pattern of disease diagnosis and significant clinical impact. (1)

Picture courtesy: (last date of access 12-07-2021)

Artificial Intelligence (AI) is a simulation of human intelligence processes like learning, reasoning, and self-correction by machines and/or computer systems. (2)

Figure 1: Types of Artificial Intelligence (AI)

(Picture courtesy: last date of access 24-05-2021)


Worldwide applications of AI in various fields (2)



  1. Vision and Refractive Error

New machine learning-based diagnostic applications for refraction, vision correction, and surgical and post-surgical management has expanded rapidly. Example: the wavefront aberrometer. Recent software of Iterative Dichotomize Three (ID3) analyses refractive variables: tear production, lenticular powers, corneal curvatures, and age to provide final prescriptions for eyeglasses. (3)

  1. Cornea And Ocular Surface

An AI algorithm has been developed to detect corneal abnormalities using the principle of pattern detection through corneal topography for early detection and acceleration of conditions as keratoconus, Terrien’s corneal abnormalities. Researchers are using machine learning techniques to analyse and diagnose early patterns for dry eye disorders, most of which will be driven by AI software and AI robotics, respectively. (3)

  1. Anterior Segment

Recent Deep learning technologies have been helpful to diagnose and make treatment decisions for congenital cataracts. Usage of AI robotic machines also has been evolving in cataract surgeries by planning surgery, calculating location of incision, minimizing error margin, and collating feedback for future operations. An AI Bayesian logic network is also being used for first or second diagnoses of various systemic conditions associated with uveitis. (3)

  1. Retina

AI has now become a valuable clinical tool in the case of Diabetic retinopathy for identifying and grading its severity. Machine learning is now also used for prediction of best correctable vision with anti-VEGF therapies in Age-related macular degeneration as well as in quantitative characterization of neovascular lesions in large-scale clinical spectral-domain optical coherence tomography datasets. (3)

  1. Glaucoma And Neuro Ophthalmic Disorders

AI-based software makes comparisons of thousands of healthy and glaucomatous optic nerve head images and hence allows machine learning to identify glaucomatous changes. (3)  Applying Machine Learning methods can improve the detection of pre-perimetric glaucoma Visual Fields from healthy Visual Fields significantly. (1)

  1. Immunotherapy, Genetics And Stem Cell Therapies

Recently, AI technologies and Robotics have been evolving in genomic medicine and health. AI can effectively play role in cancer diagnosis, therapies, immunotherapies, genetic therapies, and stem cell therapies for various congenital and acquired eye disorders causing blindness. (3)


In a clinical setting, patients suffer from multi-categorical retinal diseases so a model trained to the specific detect conditions will fail to detect another.

In the future to enhance application of AI in clinical practice, multimodal clinical imaging techniques, such as optical coherence tomography angiography, visual field, and fundus images, should be integrated to build a generalized AI system for more reliable AI diagnosis. (4)



  1. Asaoka, R., Iwase, A., Hirasawa, K., Murata, H., & Araie, M. (2014). Identifying “preperimetric” glaucoma in standard automated perimetry visual fields. Investigative ophthalmology & visual science55(12), 7814-7820.
  2. last date of excess 10/07/2021
  3. Catania, L. J., & Nicolitz, E. (2018). Artificial intelligence and its applications in vision and eye care. Advances in Ophthalmology and Optometry3(1), 21-38.
  4. Lu, W., Tong, Y., Yu, Y., Xing, Y., Chen, C., & Shen, Y. (2018). Applications of artificial intelligence in ophthalmology: general overview. Journal of ophthalmology2018.