Artificial Intelligence in Retinopathy of Prematurity Diagnosis

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

doi:10.1167/tvst.9.2.5

Artificial Intelligence in Retinopathy of Prematurity Diagnosis

Transl Vis Sci Technol. 2020 Feb 10;9(2):5. doi: 10.1167/tvst.9.2.5.

Authors

Brittni A Scruggs¹, R V Paul Chan², Jayashree Kalpathy-Cramer³, Michael F Chiang^{1

4}, J Peter Campbell^{1

4}

Affiliations

¹ Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, OR, USA.
² Department of Ophthalmology, University of Illinois, Chicago, IL, USA.
³ Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
⁴ Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Review

MeSH terms

Artificial Intelligence*
Child
Humans
Infant, Low Birth Weight
Infant, Newborn
Retinopathy of Prematurity* / diagnosis

Abstract

Publication types

MeSH terms

Grants and funding