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

doi:10.1145/3292500.3330713

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

Ophthalmology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 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 language	English (US)
Title of host publication	KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	1809-1819
Number of pages	11
ISBN (Electronic)	9781450362016
DOIs	https://doi.org/10.1145/3292500.3330713
State	Published - Jul 25 2019
Event	25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States Duration: Aug 4 2019 → Aug 8 2019

Publication series

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

Conference

Conference	25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Country/Territory	United States
City	Anchorage
Period	8/4/19 → 8/8/19

Keywords

Bradley-Terr model
Classification
Learning from comparisons
Retinopathy of Prematurity

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/3292500.3330713

Cite this

Tian, P., Ostmo, S., Guo, Y., Campbell, J. P., Kalpathy-Cramer, J., Chiang, M. F., Dy, J., Erdogmus, D., & Ioannidis, S. (2019). A severity score for retinopathy of prematurity. In KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1809-1819). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3292500.3330713

A severity score for retinopathy of prematurity. / Tian, Peng; Ostmo, Susan; Guo, Yuan et al.

KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2019. p. 1809-1819 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tian, P, Ostmo, S, Guo, Y, Campbell, JP, Kalpathy-Cramer, J, Chiang, MF, Dy, J, Erdogmus, D & Ioannidis, S 2019, A severity score for retinopathy of prematurity. in KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 1809-1819, 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019, Anchorage, United States, 8/4/19. https://doi.org/10.1145/3292500.3330713

Tian P, Ostmo S, Guo Y, Campbell JP, Kalpathy-Cramer J, Chiang MF et al. A severity score for retinopathy of prematurity. In KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2019. p. 1809-1819. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3292500.3330713

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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.",

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note = "Funding Information: Our work is supported by NIH (R01EY019474, P30EY10572), NSF (SCH-1622542 at MGH; SCH-1622536 at Northeastern; SCH-1622679 at OHSU), and by unrestricted departmental funding from Research to Prevent Blindness (OHSU). Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 ; Conference date: 04-08-2019 Through 08-08-2019",

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AU - Dy, Jennifer

AU - Erdogmus, Deniz

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A severity score for retinopathy of prematurity

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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