Fundus image quality assessment in retinopathy of prematurity via multi-label graph evidential network.

Abstract

Retinopathy of Prematurity (ROP) is a leading cause of childhood blindness worldwide. In clinical practice, fundus imaging serves as a primary diagnostic tool for ROP, making the accurate quality assessment of these images critically important. However, existing automated methods for evaluating ROP fundus images face significant challenges. First, there is a high degree of visual similarity between lesions and factors that influence quality. Second, there is a paucity of trustworthy outputs and interpretable or clinical-friendly designs, which limit their reliability and effectiveness. In this work, we propose a ROP image quality assessment framework, termed Q-ROP. This framework leverages fine-grained multi-label annotations based on key image factors such as artifacts, illumination, spatial positioning, and structural clarity. Additionally, the integration of a label graph network with evidential learning theory enables the model to explicitly capture the relationships between quality grades and influencing factors, thereby improving both robustness and accuracy. This approach facilitates interpretable analysis by directing the model's focus toward relevant image features and reducing interference from lesion-like artifacts. Furthermore, the incorporation of evidential learning theory serves to quantify the uncertainty inherent in quality ratings, thereby ensuring the trustworthiness of the assessments. Trained and tested on a dataset of 6677 ROP images across three quality levels (i.e. acceptable, potentially acceptable, and unacceptable), Q-ROP achieved state-of-the-art performance with a 95.82% accuracy. Its effectiveness was further validated in a downstream ROP staging task, where it significantly improved the performance of typical classification models. These results demonstrate Q-ROP's strong potential as a reliable and robust tool for clinical decision support.