Artificial intelligence in retinopathy of prematurity diagnosis

Brittni A. Scruggs; R. V. Paulchan; Jayashree Kalpathy-Cramer; Michael F. Chiang; J. Peter Campbell

doi:10.1167/tvst.9.2.5

Artificial intelligence in retinopathy of prematurity diagnosis

Brittni A. Scruggs, R. V. Paulchan, Jayashree Kalpathy-Cramer, Michael F. Chiang, J. Peter Campbell

Ophthalmology

Research output: Contribution to journal › Article › peer-review

54 Scopus citations

Abstract

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.

Original language	English (US)
Article number	5
Journal	Translational Vision Science and Technology
Volume	9
Issue number	2
DOIs	https://doi.org/10.1167/tvst.9.2.5
State	Published - 2020

Keywords

Artificial intelligence
Machine learning
Pediatric retina
Retinopathy of prematurity

ASJC Scopus subject areas

Biomedical Engineering
Ophthalmology

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10.1167/tvst.9.2.5

Cite this

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title = "Artificial intelligence in retinopathy of prematurity diagnosis",

abstract = "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.",

keywords = "Artificial intelligence, Machine learning, Pediatric retina, Retinopathy of prematurity",

author = "Scruggs, {Brittni A.} and Paulchan, {R. V.} and Jayashree Kalpathy-Cramer and Chiang, {Michael F.} and {Peter Campbell}, J.",

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AU - Scruggs, Brittni A.

AU - Paulchan, R. V.

AU - Kalpathy-Cramer, Jayashree

AU - Chiang, Michael F.

AU - Peter Campbell, J.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Machine learning

KW - Pediatric retina

KW - Retinopathy of prematurity

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Artificial intelligence in retinopathy of prematurity diagnosis

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Keywords

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