Literature DB >> 30225234

Application of artificial intelligence in ophthalmology.

Xue-Li Du1, Wen-Bo Li1, Bo-Jie Hu1.   

Abstract

Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading.

Entities:  

Keywords:  artificial intelligence; deep learning; images processing; machine learning; ophthalmology

Year:  2018        PMID: 30225234      PMCID: PMC6133903          DOI: 10.18240/ijo.2018.09.21

Source DB:  PubMed          Journal:  Int J Ophthalmol        ISSN: 2222-3959            Impact factor:   1.779


  72 in total

Review 1.  Automated quality assessment of retinal fundus photos.

Authors:  Jan Paulus; Jörg Meier; Rüdiger Bock; Joachim Hornegger; Georg Michelson
Journal:  Int J Comput Assist Radiol Surg       Date:  2010-05-19       Impact factor: 2.924

2.  Automated assessment of diabetic retinal image quality based on clarity and field definition.

Authors:  Alan D Fleming; Sam Philip; Keith A Goatman; John A Olson; Peter F Sharp
Journal:  Invest Ophthalmol Vis Sci       Date:  2006-03       Impact factor: 4.799

3.  Prevalence and risk factors of retinal vein occlusion in an Asian population.

Authors:  L L Lim; N Cheung; J J Wang; F M A Islam; P Mitchell; S M Saw; T Aung; T Y Wong
Journal:  Br J Ophthalmol       Date:  2008-08-06       Impact factor: 4.638

4.  Automated characterization of blood vessels as arteries and veins in retinal images.

Authors:  Qazaleh Mirsharif; Farshad Tajeripour; Hamidreza Pourreza
Journal:  Comput Med Imaging Graph       Date:  2013-07-10       Impact factor: 4.790

5.  Automatic intraocular lens segmentation and detection in optical coherence tomography images.

Authors:  Melanie Gillner; Timo Eppig; Achim Langenbucher
Journal:  Z Med Phys       Date:  2013-08-06       Impact factor: 4.820

6.  Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects.

Authors:  Hassan Muhammad; Thomas J Fuchs; Nicole De Cuir; Carlos G De Moraes; Dana M Blumberg; Jeffrey M Liebmann; Robert Ritch; Donald C Hood
Journal:  J Glaucoma       Date:  2017-12       Impact factor: 2.503

7.  Revised indications for the treatment of retinopathy of prematurity: results of the early treatment for retinopathy of prematurity randomized trial.

Authors: 
Journal:  Arch Ophthalmol       Date:  2003-12

8.  Sensitivity and specificity of optic disc parameters in chronic glaucoma.

Authors:  T Damms; F Dannheim
Journal:  Invest Ophthalmol Vis Sci       Date:  1993-06       Impact factor: 4.799

9.  Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters.

Authors:  Kazuko Omodaka; Guangzhou An; Satoru Tsuda; Yukihiro Shiga; Naoko Takada; Tsutomu Kikawa; Hidetoshi Takahashi; Hideo Yokota; Masahiro Akiba; Toru Nakazawa
Journal:  PLoS One       Date:  2017-12-19       Impact factor: 3.240

10.  Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.

Authors:  Esra Ataer-Cansizoglu; Veronica Bolon-Canedo; J Peter Campbell; Alican Bozkurt; Deniz Erdogmus; Jayashree Kalpathy-Cramer; Samir Patel; Karyn Jonas; R V Paul Chan; Susan Ostmo; Michael F Chiang
Journal:  Transl Vis Sci Technol       Date:  2015-11-30       Impact factor: 3.283

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  14 in total

Review 1.  Artificial Intelligence: Review of Current and Future Applications in Medicine.

Authors:  L Brannon Thomas; Stephen M Mastorides; Narayan A Viswanadhan; Colleen E Jakey; Andrew A Borkowski
Journal:  Fed Pract       Date:  2021-11

2.  Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography.

Authors:  Papis Wongchaisuwat; Ranida Thamphithak; Peerakarn Jitpukdee; Nida Wongchaisuwat
Journal:  Transl Vis Sci Technol       Date:  2022-10-03       Impact factor: 3.048

3.  Development and Validation of Machine Learning Models: Electronic Health Record Data To Predict Visual Acuity After Cataract Surgery.

Authors:  Stacey E Alexeeff; Stephen Uong; Liyan Liu; Neal H Shorstein; James Carolan; Laura B Amsden; Lisa J Herrinton
Journal:  Perm J       Date:  2020-12

4.  Artificial intelligence method to classify ophthalmic emergency severity based on symptoms: a validation study.

Authors:  Hyunmin Ahn
Journal:  BMJ Open       Date:  2020-07-05       Impact factor: 2.692

5.  Artificial intelligence in ophthalmology: Is it just hype with no substance or the real McCoy.

Authors:  Santosh V Patil
Journal:  Indian J Ophthalmol       Date:  2019-07       Impact factor: 1.848

6.  Commentary: Rise of machine learning and artificial intelligence in ophthalmology.

Authors:  John Davis Akkara; Anju Kuriakose
Journal:  Indian J Ophthalmol       Date:  2019-07       Impact factor: 1.848

7.  Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology.

Authors:  Alejandro Rodríguez-González; Massimiliano Zanin; Ernestina Menasalvas-Ruiz
Journal:  Yearb Med Inform       Date:  2019-08-16

Review 8.  Insights into the growing popularity of artificial intelligence in ophthalmology.

Authors:  Sreetama Dutt; Anand Sivaraman; Florian Savoy; Ramachandran Rajalakshmi
Journal:  Indian J Ophthalmol       Date:  2020-07       Impact factor: 1.848

9.  Impact of Artificial Intelligence on Medical Education in Ophthalmology.

Authors:  Nita G Valikodath; Emily Cole; Daniel S W Ting; J Peter Campbell; Louis R Pasquale; Michael F Chiang; R V Paul Chan
Journal:  Transl Vis Sci Technol       Date:  2021-06-01       Impact factor: 3.283

Review 10.  The Current State of Artificial Intelligence in Medical Imaging and Nuclear Medicine.

Authors:  Louise I T Lee; Senthooran Kanthasamy; Radha S Ayyalaraju; Rakesh Ganatra
Journal:  BJR Open       Date:  2019-10-16
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