Abstract
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.