1. Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks
- Author
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Aaron S. Coyner, Ryan Swan, J. Peter Campbell, Susan Ostmo, James M. Brown, Jayashree Kalpathy-Cramer, Sang Jin Kim, Karyn E. Jonas, R.V. Paul Chan, Michael F. Chiang, Kemal Sonmez, R. V. Paul Chan, Karyn Jonas, Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Anton Orlin, Audina Berrocal, Catherin Negron, Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Evan Kruger, Kathryn McGovern, Charles Simmons, Raghu Murthy, Sharon Galvis, Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Ken Chang, Andrew Beers, Deniz Erdogmus, Stratis Ioannidis, Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza
- Subjects
Male ,medicine.medical_specialty ,Convolutional neural network ,Article ,Ophthalmology ,medicine ,Image Processing, Computer-Assisted ,Humans ,Generalizability theory ,Retinopathy of Prematurity ,Medical diagnosis ,Rank correlation ,Receiver operating characteristic ,business.industry ,Infant, Newborn ,Pattern recognition ,Ophthalmoscopy ,Ranking ,ROC Curve ,Test set ,Pairwise comparison ,Female ,Artificial intelligence ,Neural Networks, Computer ,business ,Algorithms - Abstract
Purpose Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of this study was to implement a deep convolutional neural network (CNN) for automated assessment of fundus image quality in retinopathy of prematurity (ROP). Design Experimental study. Participants Retinal fundus images were collected from preterm infants during routine ROP screenings. Methods Six thousand one hundred thirty-nine retinal fundus images were collected from 9 academic institutions. Each image was graded for quality (acceptable quality [AQ], possibly acceptable quality [PAQ], or not acceptable quality [NAQ]) by 3 independent experts. Quality was defined as the ability to assess an image confidently for the presence of ROP. Of the 6139 images, NAQ, PAQ, and AQ images represented 5.6%, 43.6%, and 50.8% of the image set, respectively. Because of low representation of NAQ images in the data set, images labeled NAQ were grouped into the PAQ category, and a binary CNN classifier was trained using 5-fold cross-validation on 4000 images. A test set of 2109 images was held out for final model evaluation. Additionally, 30 images were ranked from worst to best quality by 6 experts via pairwise comparisons, and the CNN’s ability to rank quality, regardless of quality classification, was assessed. Main Outcome Measures The CNN performance was evaluated using area under the receiver operating characteristic curve (AUC). A Spearman’s rank correlation was calculated to evaluate the overall ability of the CNN to rank images from worst to best quality as compared with experts. Results The mean AUC for 5-fold cross-validation was 0.958 (standard deviation, 0.005) for the diagnosis of AQ versus PAQ images. The AUC was 0.965 for the test set. The Spearman’s rank correlation coefficient on the set of 30 images was 0.90 as compared with the overall expert consensus ranking. Conclusions This model accurately assessed retinal fundus image quality in a comparable manner with that of experts. This fully automated model has potential for application in clinical settings, telemedicine, and computer-based image analysis in ROP and for generalizability to other ophthalmic diseases.
- Published
- 2019