Literature DB >> 30155978

Current state and future prospects of artificial intelligence in ophthalmology: a review.

Daniel T Hogarty1, David A Mackey1,2,3, Alex W Hewitt1,2,3.   

Abstract

Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although AI has broad application across many medical fields, it will have particular utility in ophthalmology and will dramatically change the diagnostic and treatment pathways for many eye conditions such as corneal ectasias, glaucoma, age-related macular degeneration and diabetic retinopathy. However, given that AI has primarily been driven as a computer science, its concepts and terminology are unfamiliar to many medical professionals. Important key terms such as machine learning and deep learning are often misunderstood and incorrectly used interchangeably. This article presents an overview of AI and new developments relevant to ophthalmology.
© 2018 Royal Australian and New Zealand College of Ophthalmologists.

Entities:  

Keywords:  artificial intelligence; deep learning; diabetic retinopathy; machine learning; ophthalmology

Mesh:

Year:  2018        PMID: 30155978     DOI: 10.1111/ceo.13381

Source DB:  PubMed          Journal:  Clin Exp Ophthalmol        ISSN: 1442-6404            Impact factor:   4.207


  19 in total

Review 1.  Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences.

Authors:  Blake S Wilson; Debara L Tucci; David A Moses; Edward F Chang; Nancy M Young; Fan-Gang Zeng; Nicholas A Lesica; Andrés M Bur; Hannah Kavookjian; Caroline Mussatto; Joseph Penn; Sara Goodwin; Shannon Kraft; Guanghui Wang; Jonathan M Cohen; Geoffrey S Ginsburg; Geraldine Dawson; Howard W Francis
Journal:  J Assoc Res Otolaryngol       Date:  2022-04-20

2.  Ocular blood flow as a clinical observation: Value, limitations and data analysis.

Authors:  Alon Harris; Giovanna Guidoboni; Brent Siesky; Sunu Mathew; Alice C Verticchio Vercellin; Lucas Rowe; Julia Arciero
Journal:  Prog Retin Eye Res       Date:  2020-01-24       Impact factor: 21.198

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.  Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks.

Authors:  Jiewei Jiang; Liming Wang; Haoran Fu; Erping Long; Yibin Sun; Ruiyang Li; Zhongwen Li; Mingmin Zhu; Zhenzhen Liu; Jingjing Chen; Zhuoling Lin; Xiaohang Wu; Dongni Wang; Xiyang Liu; Haotian Lin
Journal:  Ann Transl Med       Date:  2021-04

Review 5.  The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.

Authors:  Claire M Felmingham; Nikki R Adler; Zongyuan Ge; Rachael L Morton; Monika Janda; Victoria J Mar
Journal:  Am J Clin Dermatol       Date:  2021-03       Impact factor: 7.403

6.  Pathological Myopia Image Recognition Strategy Based on Data Augmentation and Model Fusion.

Authors:  Jianfeng Cui; Xiaoyun Zhang; Feibing Xiong; Chin-Ling Chen
Journal:  J Healthc Eng       Date:  2021-05-05       Impact factor: 2.682

7.  Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.

Authors:  Avyuk Dixit; Jithin Yohannan; Michael V Boland
Journal:  Ophthalmology       Date:  2020-12-25       Impact factor: 14.277

Review 8.  Review of Machine Learning in Predicting Dermatological Outcomes.

Authors:  Amy X Du; Sepideh Emam; Robert Gniadecki
Journal:  Front Med (Lausanne)       Date:  2020-06-12

9.  Digital image processing software for diagnosing diabetic retinopathy from fundus photograph.

Authors:  Tanapat Ratanapakorn; Athiwath Daengphoonphol; Nawapak Eua-Anant; Yosanan Yospaiboon
Journal:  Clin Ophthalmol       Date:  2019-04-17

10.  Detection of Fuchs' Uveitis Syndrome From Slit-Lamp Images Using Deep Convolutional Neural Networks in a Chinese Population.

Authors:  Wanyun Zhang; Zhijun Chen; Han Zhang; Guannan Su; Rui Chang; Lin Chen; Ying Zhu; Qingfeng Cao; Chunjiang Zhou; Yao Wang; Peizeng Yang
Journal:  Front Cell Dev Biol       Date:  2021-06-18
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