000008827 001__ 8827
000008827 005__ 20231129124947.0
000008827 0247_ $$2DOI$$a10.6083/1g05fc41f
000008827 037__ $$aETD
000008827 245__ $$aMachine learning for disease detection and prediction in retinopathy of prematurity
000008827 260__ $$bOregon Health and Science University
000008827 269__ $$a2021
000008827 336__ $$aDissertation
000008827 502__ $$bPh.D.
000008827 520__ $$aAn estimated one in ten babies, 15 million worldwide, are born prematurely each year - prematurity being a significant risk factor for the development of retinopathy of prematurity (ROP), a potentially-blinding disorder of the retinal vasculature. Despite the existence of known risk factors and many effective treatment options, ROP remains one of the world's leading causes of childhood blindness. This is primarily due to a combination of the scarcity of ROP experts and advances in neonatal intensive care units, which have increased the survival rate of younger, smaller infants and, consequently, the incidence and prevalence of ROP.
000008827 542__ $$fIn copyright - single owner
000008827 650__ $$aOphthalmology$$023229
000008827 650__ $$aRetinopathy of Prematurity$$025405
000008827 650__ $$aArtificial Intelligence$$015109
000008827 650__ $$aMachine Learning$$011449
000008827 650__ $$aDeep Learning$$012734
000008827 650__ $$aTelemedicine$$029784
000008827 691__ $$aSchool of Medicine$$041369
000008827 692__ $$aDepartment of Medical Informatics and Clinical Epidemiology$$041422
000008827 7001_ $$aCoyner, Aaron S.
000008827 8564_ $$9e62963cc-d8c6-487c-bc5a-53b6b903fd41$$s2738486$$uhttps://digitalcollections.ohsu.edu/record/8827/files/Coyner.Aaron.2021.pdf
000008827 905__ $$a/rest/prod/1g/05/fc/41/1g05fc41f
000008827 909CO $$ooai:digitalcollections.ohsu.edu:8827$$pstudent-work
000008827 980__ $$aTheses and Dissertations