Artificial Intelligence in Retinopathy of Prematurity Diagnosis

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The diagnosis of ROP is subclassified by zone, stage, and plus disease, with each area demonstrating significant intra- and interexpert subjectivity and disagreement. In addition to improved efficiencies for ROP sc...

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Autores principales: Scruggs, Brittni A., Chan, R. V. Paul, Kalpathy-Cramer, Jayashree, Chiang, Michael F., Campbell, J. Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2020
Materias:
Special Issue
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7343673/
https://www.ncbi.nlm.nih.gov/pubmed/32704411
http://dx.doi.org/10.1167/tvst.9.2.5
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author Scruggs, Brittni A.
Chan, R. V. Paul
Kalpathy-Cramer, Jayashree
Chiang, Michael F.
Campbell, J. Peter
author_facet Scruggs, Brittni A.
Chan, R. V. Paul
Kalpathy-Cramer, Jayashree
Chiang, Michael F.
Campbell, J. Peter
author_sort Scruggs, Brittni A.
collection PubMed
description Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The diagnosis of ROP is subclassified by zone, stage, and plus disease, with each area demonstrating significant intra- and interexpert subjectivity and disagreement. In addition to improved efficiencies for ROP screening, artificial intelligence may lead to automated, quantifiable, and objective diagnosis in ROP. This review focuses on the development of artificial intelligence for automated diagnosis of plus disease in ROP and highlights the clinical and technical challenges of both the development and implementation of artificial intelligence in the real world.
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spelling pubmed-73436732020-07-22 Artificial Intelligence in Retinopathy of Prematurity Diagnosis Scruggs, Brittni A. Chan, R. V. Paul Kalpathy-Cramer, Jayashree Chiang, Michael F. Campbell, J. Peter Transl Vis Sci Technol Special Issue Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The diagnosis of ROP is subclassified by zone, stage, and plus disease, with each area demonstrating significant intra- and interexpert subjectivity and disagreement. In addition to improved efficiencies for ROP screening, artificial intelligence may lead to automated, quantifiable, and objective diagnosis in ROP. This review focuses on the development of artificial intelligence for automated diagnosis of plus disease in ROP and highlights the clinical and technical challenges of both the development and implementation of artificial intelligence in the real world. The Association for Research in Vision and Ophthalmology 2020-02-10 /pmc/articles/PMC7343673/ /pubmed/32704411 http://dx.doi.org/10.1167/tvst.9.2.5 Text en Copyright 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Special Issue
Scruggs, Brittni A.
Chan, R. V. Paul
Kalpathy-Cramer, Jayashree
Chiang, Michael F.
Campbell, J. Peter
Artificial Intelligence in Retinopathy of Prematurity Diagnosis
title Artificial Intelligence in Retinopathy of Prematurity Diagnosis
title_full Artificial Intelligence in Retinopathy of Prematurity Diagnosis
title_fullStr Artificial Intelligence in Retinopathy of Prematurity Diagnosis
title_full_unstemmed Artificial Intelligence in Retinopathy of Prematurity Diagnosis
title_short Artificial Intelligence in Retinopathy of Prematurity Diagnosis
title_sort artificial intelligence in retinopathy of prematurity diagnosis
topic Special Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7343673/
https://www.ncbi.nlm.nih.gov/pubmed/32704411
http://dx.doi.org/10.1167/tvst.9.2.5
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