%0 Journal Article
%T Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity
%A Aaron S. Coyner
%A J. Peter Campbell
%A James M. Brown
%A Jayashree Kalpathy-Cramer
%A Karyn E. Jonas
%A Michael F. Chiang
%A R.V. Paul Chan
%A Ryan Swan
%A Sang Jin Kim
%A Susan Ostmo
%J Archive of "AMIA Annual Symposium Proceedings".
%D 2018
%X Accurate image-based medical diagnosis relies upon adequate image quality and clarity. This has important implications for clinical diagnosis, and for emerging methods such as telemedicine and computer-based image analysis. In this study, we trained a convolutional neural network (CNN) to automatically assess the quality of retinal fundus images in a representative ophthalmic disease, retinopathy of prematurity (ROP). 6,043 wide-angle fundus images were collected from preterm infants during routine ROP screening examinations. Images were assessed by clinical experts for quality regarding ability to diagnose ROP accurately, and were labeled ¡°acceptable¡± or ¡°not acceptable.¡± The CNN training, validation and test sets consisted of 2,770 images, 200 images, and 3,073 images, respectively. Test set accuracy was 89.1%, with area under the receiver operating curve equal to 0.964, and area under the precision-recall curve equal to 0.966. Taken together, our CNN shows promise as a useful prescreening method for telemedicine and computer-based image analysis applications. We feel this methodology is generalizable to all clinical domains involving image-based diagnosis
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371336/