Retinopathy of prematurity(ROP)is the primary cause of preventable childhood blindness. It is hard to screen, diagnose and objectively evaluate. There are various modalities for ROP screening, including various contact or non-contact imaging devices, smart phone-based fundus photography, and artificial intelligence-based fundus image analysis. The diagnosis of ROP is based on visualization and recording of the entire retinal fundus of ROP, which is also the basis for subsequent screening, treatment assessment. Fundus screening is critical for early recognition and facilitates early detection and prompt referral. Potential features may be found by analyzing and summarizing the characteristics of ROP fundus images. Subsequently, timely and targeted ROP prevention and treatment could be performed. Artificial intelligence promotes automatic, quantifiable and objective diagnosis of ROP. This article reviews commonly used clinical fundus examination methods and fundus image characteristics of ROP and summarizes the latest research progress on the application of artificial intelligence in the automatic diagnosis of ROP.