Literature DB >> 31149787

Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology.

Lokman Balyen1, Tunde Peto2.   

Abstract

The lifestyle of modern society has changed significantly with the emergence of artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies in recent years. Artificial intelligence is a multidimensional technology with various components such as advanced algorithms, ML and DL. Together, AI, ML, and DL are expected to provide automated devices to ophthalmologists for early diagnosis and timely treatment of ocular disorders in the near future. In fact, AI, ML, and DL have been used in ophthalmic setting to validate the diagnosis of diseases, read images, perform corneal topographic mapping and intraocular lens calculations. Diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are the 3 most common causes of irreversible blindness on a global scale. Ophthalmic imaging provides a way to diagnose and objectively detect the progression of a number of pathologies including DR, AMD, glaucoma, and other ophthalmic disorders. There are 2 methods of imaging used as diagnostic methods in ophthalmic practice: fundus digital photography and optical coherence tomography (OCT). Of note, OCT has become the most widely used imaging modality in ophthalmology settings in the developed world. Changes in population demographics and lifestyle, extension of average lifespan, and the changing pattern of chronic diseases such as obesity, diabetes, DR, AMD, and glaucoma create a rising demand for such images. Furthermore, the limitation of availability of retina specialists and trained human graders is a major problem in many countries. Consequently, given the current population growth trends, it is inevitable that analyzing such images is time-consuming, costly, and prone to human error. Therefore, the detection and treatment of DR, AMD, glaucoma, and other ophthalmic disorders through unmanned automated applications system in the near future will be inevitable. We provide an overview of the potential impact of the current AI, ML, and DL methods and their applications on the early detection and treatment of DR, AMD, glaucoma, and other ophthalmic diseases. Copyright 2019 Asia-Pacific Academy of Ophthalmology.

Entities:  

Keywords:  age-related macular degeneration; deep learning; diabetic retinopathy; glaucoma; machine learning

Mesh:

Year:  2019        PMID: 31149787     DOI: 10.22608/APO.2018479

Source DB:  PubMed          Journal:  Asia Pac J Ophthalmol (Phila)        ISSN: 2162-0989


  28 in total

Review 1.  Applications of augmented reality in ophthalmology [Invited].

Authors:  Güneş Aydındoğan; Koray Kavaklı; Afsun Şahin; Pablo Artal; Hakan Ürey
Journal:  Biomed Opt Express       Date:  2020-12-21       Impact factor: 3.732

Review 2.  Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation - A Review.

Authors:  Mohammed Alawad; Abdulrhman Aljouie; Suhailah Alamri; Mansour Alghamdi; Balsam Alabdulkader; Norah Alkanhal; Ahmed Almazroa
Journal:  Clin Ophthalmol       Date:  2022-03-11

3.  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

Review 4.  Understanding required to consider AI applications to the field of ophthalmology.

Authors:  Hitoshi Tabuchi
Journal:  Taiwan J Ophthalmol       Date:  2022-04-13

5.  Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis.

Authors:  Junqiang Zhao; Yi Lu; Shaojun Zhu; Keran Li; Qin Jiang; Weihua Yang
Journal:  Front Pharmacol       Date:  2022-06-08       Impact factor: 5.988

6.  Design of Intelligent Diagnosis and Treatment System for Ophthalmic Diseases Based on Deep Neural Network Model.

Authors:  Huihui Zhou
Journal:  Contrast Media Mol Imaging       Date:  2022-07-05       Impact factor: 3.009

7.  Eye diseases during pregnancy: a study with the medical data warehouse in the eye clinic of the Ludwig-Maximilians-Universität München in Munich in Germany.

Authors:  Thiago Gonçalves Dos Santos Martins; Paulo Schor; Luís Guilherme Arneiro Mendes; Andreas Anschütz; Rufino Silva
Journal:  Einstein (Sao Paulo)       Date:  2022-05-06

Review 8.  Current status and future trends of clinical diagnoses via image-based deep learning.

Authors:  Jie Xu; Kanmin Xue; Kang Zhang
Journal:  Theranostics       Date:  2019-10-12       Impact factor: 11.556

9.  Precautionary measures needed for ophthalmologists during pandemic of the coronavirus disease 2019 (COVID-19).

Authors:  Kelvin H Wan; Suber S Huang; Alvin L Young; Dennis Shun Chiu Lam
Journal:  Acta Ophthalmol       Date:  2020-05       Impact factor: 3.761

Review 10.  COVID-19: Ocular Manifestations and the APAO Prevention Guidelines for Ophthalmic Practices.

Authors:  Raymond L M Wong; Daniel S W Ting; Kelvin H Wan; Kenny H W Lai; Chung-Nga Ko; Paisan Ruamviboonsuk; Suber S Huang; Dennis S C Lam; Clement C Y Tham
Journal:  Asia Pac J Ophthalmol (Phila)       Date:  2020 Jul-Aug
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