Deep Learning Convolutional Networks for Image Quality Assessment in Ultra-widefield Fluorescein Angiography

Purpose - Ultra-widefield fluorescein angiography (UWFA) images are used to assess retinal, vascular, and choroidal abnormalities in retinal disease. During image acquisition, images are taken in sequential time points, which allows for interrogation of vascular features, as well as other pathologies, such as leakage. Variations in eye positioning, injection, and camera positioning all contribute to variability in image quality. The purpose of this study was to evaluate the feasibility of automated image quality classification and selection using deep learning. Methods - The images for this analysis were composed of 3543 UWFA images obtained during standard UWFA image acquisition. Ground truth image quality was assessed by expert image review, and classified into one of four categories (ungradable, poor, good, or best. 3543 images were used to train the model. A testing set composed of 392 images was used to assess model performance. Results - By expert review of 3935 images, 110 (2.8%) were graded as best, 1042 (26.5%) as good, 1156 (29.4%) as poor and 1627 (41.3%) were ungradable. In the testing set, the automated quality assessment system showed an overall accuracy of 88% for recognizing between gradable and ungradable images, and 77% accuracy for four-category classification. The receiver operating characteristic (ROC) curve measuring performance of two-class classification (ungradable and gradable) had an AUC of 0.945. Conclusions - We created a deep learning classification model that automatic classified UWFA images by quality category. The high degree of accuracy provides evidence that this method could be used to enhance the acquisition of angiogram images and speed up clinic workflow. This could result in reduced manual image grading workload, allow quality-based image presentation to clinicians, and provide near-instantaneous feedback on image quality during image acquisition for photographers.