Importance: Ultra-widefield fundus photography (UWF-FP) has shown utility in
sickle cell retinopathy screening; however, image artifact may diminish quality
and gradeability of images. Objective: To create an automated algorithm for
UWF-FP artifact classification. Design: A neural network based automated
artifact detection algorithm was designed to identify commonly encountered
UWF-FP artifacts in a cross section of patient UWF-FP. A pre-trained ResNet-50
neural network was trained on a subset of the images and the classification
accuracy, sensitivity, and specificity were quantified on the hold out test
set. Setting: The study is based on patients from a tertiary care hospital
site. Participants: There were 243 UWF-FP acquired from patients with sickle
cell disease (SCD), and artifact labelling in the following categories was
performed: Eyelash Present, Lower Eyelid Obstructing, Upper Eyelid Obstructing,
Image Too Dark, Dark Artifact, and Image Not Centered. Results: Overall, the
accuracy for each class was Eyelash Present at 83.7%, Lower Eyelid Obstructing
at 83.7%, Upper Eyelid Obstructing at 98.0%, Image Too Dark at 77.6%, Dark
Artifact at 93.9%, and Image Not Centered at 91.8%. Conclusions and Relevance:
This automated algorithm shows promise in identifying common imaging artifacts
on a subset of Optos UWF-FP in SCD patients. Further refinement is ongoing with
the goal of improving efficiency of tele-retinal screening in sickle cell
retinopathy (SCR) by providing a photographer real-time feedback as to the
types of artifacts present, and the need for image re-acquisition. This
algorithm also may have potential future applicability in other retinal
diseases by improving quality and efficiency of image acquisition of UWF-FP.

本研究开发了一种基于神经网络的自动化算法，识别常见的超广角眼底照相术图像伪影，具有潜在的用于提高疾病检测质量和效率的应用价值。