A severity score for retinopathy of prematurity

Peng Tian, Susan Ostmo, Yuan Guo, John Peter Campbell, Jayashree Kalpathy-Cramer, Michael F. Chiang, Jennifer Dy, Deniz Erdogmus, Stratis Ioannidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Retinopathy of Prematurity (ROP) is a leading cause for childhood blindness worldwide. An automated ROP detection system could significantly improve the chance of a child receiving proper diagnosis and treatment. We propose a means of producing a continuous severity score in an automated fashion, regressed from both (a) diagnostic class labels as well as (b) comparison outcomes. Our generative model combines the two sources, and successfully addresses inherent variability in diagnostic outcomes. In particular, our method exhibits an excellent predictive performance of both diagnostic and comparison outcomes over a broad array of metrics, including AUC, precision, and recall.

Original languageEnglish (US)
Title of host publicationKDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1809-1819
Number of pages11
ISBN (Electronic)9781450362016
DOIs
StatePublished - Jul 25 2019
Event25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States
Duration: Aug 4 2019Aug 8 2019

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Country/TerritoryUnited States
CityAnchorage
Period8/4/198/8/19

Keywords

  • Bradley-Terr model
  • Classification
  • Learning from comparisons
  • Retinopathy of Prematurity

ASJC Scopus subject areas

  • Software
  • Information Systems

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