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.

翻译：重要性：超广角眼底照相（UWF-FP）在镰状细胞视网膜病变筛查中具有应用价值，但图像伪影可能降低图像质量与可分级性。目的：开发一种用于UWF-FP伪影分类的自动化算法。设计：设计基于神经网络的伪影自动检测算法，用于识别患者UWF-FP横断面研究中常见的伪影类型。采用预训练ResNet-50神经网络对部分图像进行训练，并在独立测试集上量化分类准确率、敏感性与特异性。场所：本研究基于某三级医疗机构的患者数据。参与者：共采集243例镰状细胞病（SCD）患者的UWF-FP图像，并按以下类别进行伪影标注：睫毛遮挡、下眼睑遮挡、上眼睑遮挡、图像过暗、暗色伪影及图像未居中。结果：各类别分类准确率分别为：睫毛遮挡83.7%、下眼睑遮挡83.7%、上眼睑遮挡98.0%、图像过暗77.6%、暗色伪影93.9%、图像未居中91.8%。结论与意义：该自动化算法在识别SCD患者Optos UWF-FP图像常见伪影方面展现出潜力。当前正持续优化算法，旨在通过为摄影师提供伪影类型实时反馈及是否需要重新采集图像的提示，提升镰状细胞视网膜病变（SCR）远程筛查效率。此外，该算法未来有望推广至其他视网膜疾病，通过提高UWF-FP图像采集质量与效率实现更广泛临床应用。