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©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Med Imaging. Feb 28, 2021; 2(1): 5-12
Published online Feb 28, 2021. doi: 10.35711/aimi.v2.i1.5
Published online Feb 28, 2021. doi: 10.35711/aimi.v2.i1.5
Artificial intelligence in ophthalmology: A new era is beginning
Bijnya Birajita Panda, Subhodeep Thakur, Sumita Mohapatra, Subhabrata Parida, Department ofOphthalmology, S.C.B Medical College and Hospital, Cuttack 753007, Odisha, India
Author contributions: Panda BB and Thakur S designed the review, collected the data, analyzed the data and prepared the initial draft. Mohapatra S and Parida S revised the manuscript for important intellectual content and prepared the final draft; All authors read the manuscript and approved for the publication.
Conflict-of-interest statement: The authors declare no conflicts of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bijnya Birajita Panda, MBBS, MS, Assistant Professor, Ophthalmology, S.C.B Medical College and Hospital, Manglabag, Cuttack 753007, Odisha, India. bigyan_panda@yahoo.co.in
Received: November 28, 2020
Peer-review started: November 28, 2020
First decision: December 18, 2020
Revised: December 31, 2020
Accepted: February 12, 2021
Article in press: February 12, 2021
Published online: February 28, 2021
Peer-review started: November 28, 2020
First decision: December 18, 2020
Revised: December 31, 2020
Accepted: February 12, 2021
Article in press: February 12, 2021
Published online: February 28, 2021
Core Tip
Core Tip: Artificial intelligence has improved the diagnostic ability in the ophthalmology field, thereby improving patient care. The in-depth image recognition in diabetic retinopathy, retinopathy of prematurity and age-related macular degeneration has helped in early diagnosis and prevention. The detection of visual filed defect even at its minute stage in glaucoma and other ocular conditions has accurately staged the disease with the prediction of its severity. Still, many challenges need to be addressed, such as image incorporation, training sets and the black box dilemma. Nevertheless, despite the existing differences, there is always a chance of improving machines to potentiate their efficacy and standards.