Wang Helei, Li Longhui, Wang Wenjuan, Li Zhiwen, Jian Tianzi, Yang Xueying, Song Boxuan, Li Shiqiang, Xu Fabao, Liu Shaopeng, Li Ying
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
Department of Ophthalmology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China.
Sci Rep. 2025 Jul 23;15(1):26852. doi: 10.1038/s41598-025-10859-5.
Retinopathy of prematurity (ROP) is a significant cause of childhood blindness. Many healthcare institutions face a shortage of well-trained ophthalmologists for conducting screenings. Hence, we have developed the Deep Learning Infant Fundus Quality Feedback System (DLIF-QFS) to assess the overall quality of infant retinal photographs and detect common operational errors to support ROP screening and diagnosis. Our DLIF-QFS has been developed and rigorously validated using datasets comprising 13,372 images. In terms of overall quality classification, the DLIF-QFS demonstrated remarkable performance. The area under the curve (AUC) values for discriminating poor quality, adequate quality, and excellent quality images in the external validation dataset were 0.802, 0.691, and 0.926, respectively. For most classification tasks related to identifying issues in adequate and poor quality images, the AUC values consistently exceeded 0.8. In expert diagnostic tests, the DLIF-QFS improved accuracy and enhanced consistency. Its capability to identify the causes of poor image quality, enhance image quality and assist clinicians in improving diagnostic efficiency makes it a valuable tool for advancing ROP diagnosis.
早产儿视网膜病变（ROP）是儿童失明的一个重要原因。许多医疗机构面临着缺乏训练有素的眼科医生来进行筛查的问题。因此，我们开发了深度学习婴儿眼底质量反馈系统（DLIF-QFS），以评估婴儿视网膜照片的整体质量，并检测常见的操作错误，以支持ROP的筛查和诊断。我们的DLIF-QFS已经使用包含13372张图像的数据集进行了开发和严格验证。在整体质量分类方面，DLIF-QFS表现出色。在外部验证数据集中，区分质量差、质量合格和质量优秀图像的曲线下面积（AUC）值分别为0.802、0.691和0.926。对于大多数与识别质量合格和质量差的图像中的问题相关的分类任务，AUC值始终超过0.8。在专家诊断测试中，DLIF-QFS提高了准确性并增强了一致性。它识别图像质量差的原因、提高图像质量以及协助临床医生提高诊断效率的能力使其成为推进ROP诊断的有价值工具。
