Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 Sep 1;32(5):425-430.
doi: 10.1097/ICU.0000000000000788.

Artificial intelligence and ophthalmic surgery

Kapil Mishra  1 , Theodore Leng
Affiliations
Review

Artificial intelligence and ophthalmic surgery

Kapil Mishra et al. Curr Opin Ophthalmol. .

Abstract

Purpose of review: Artificial intelligence and deep learning have become important tools in extracting data from ophthalmic surgery to evaluate, teach, and aid the surgeon in all phases of surgical management. The purpose of this review is to highlight the ever-increasing intersection of computer vision, machine learning, and ophthalmic microsurgery.

Recent findings: Deep learning algorithms are being applied to help evaluate and teach surgical trainees. Artificial intelligence tools are improving real-time surgical instrument tracking, phase segmentation, as well as enhancing the safety of robotic-assisted vitreoretinal surgery.

Summary: Similar to strides appreciated in ophthalmic medical disease, artificial intelligence will continue to become an important part of surgical management of ocular conditions. Machine learning applications will help push the boundaries of what surgeons can accomplish to improve patient outcomes.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest Disclosures: None

Figures

Figure 1:
Figure 1:
Instrument Tracking in Cataract Surgery. Tool trajectories are mapped using multiple green points on the instruments during capsulorhexis for both the cystotome needle (A) and Utrata forceps (B). Images courtesy of Shameema Sikder, M.D.
See this image and copyright information in PMC

References

    1. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574–582. - PubMed
    1. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. - PMC - PubMed
    1. Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An interpretable machine learning model for accurate prediction of sepsis in the icu. Crit Care Med. 2018;46(4):547–553. - PMC - PubMed
    1. Rathi S, Tsui E, Mehta N, Zahid S, Schuman JS. The current state of teleophthalmology in the united states. Ophthalmology. 2017;124(12):1729–1734. - PMC - PubMed
    1. Abràmoff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200–5206. - PubMed