Peng Tian, Yuan Guo, Jayashree Kalpathy-Cramer, S. Ostmo, J. P. Campbell, M. Chiang, Jennifer G. Dy, Deniz Erdoğmuş, Stratis Ioannidis
{"title":"A Severity Score for Retinopathy of Prematurity","authors":"Peng Tian, Yuan Guo, Jayashree Kalpathy-Cramer, S. Ostmo, J. P. Campbell, M. Chiang, Jennifer G. Dy, Deniz Erdoğmuş, Stratis Ioannidis","doi":"10.1145/3292500.3330713","DOIUrl":null,"url":null,"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.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3292500.3330713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
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.