@article{ETD,
      recid = {8827},
      author = {Coyner, Aaron S.},
      title = {Machine learning for disease detection and prediction in  retinopathy of prematurity},
      publisher = {Oregon Health and Science University},
      school = {Ph.D.},
      address = {2021},
      number = {ETD},
      abstract = {An 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.},
      url = {http://digitalcollections.ohsu.edu/record/8827},
      doi = {https://doi.org/10.6083/1g05fc41f},
}