Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) models face challenges like accuracy and overfitting. Recent advancements in deep learning (DL), especially convolutional neural networks (CNNs), have significantly improved ROP detection and classification. The i-ROP deep learning (i-ROP-DL) system also shows promise in detecting plus disease, offering reliable ROP diagnosis potential. This research comprehensively examines the contemporary progress and challenges associated with using retinal imaging and artificial intelligence (AI) to detect ROP, offering valuable insights that can guide further investigation in this domain. Based on 89 original studies in this field (out of 1487 studies that were comprehensively reviewed), we concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions. AI holds great promise for improving ROP management. This review explores AI's potential in ROP detection, classification, diagnosis, and prognosis.

翻译：早产儿视网膜病变（ROP）是一种严重影响早产儿的疾病，表现为视网膜血管异常生长、视网膜脱离并可能导致失明。尽管半自动化系统曾通过量化视网膜血管特征辅助诊断ROP相关附加病变，但传统机器学习模型面临准确性及过拟合等挑战。近年来深度学习尤其是卷积神经网络的进展显著提升了ROP的检测与分类能力。i-ROP深度学习系统在检测附加病变方面展现出潜力，为可靠诊断ROP提供了可能。本研究全面审视了利用视网膜成像与人工智能检测ROP的当代进展与挑战，为该领域的进一步探索提供重要见解。基于对1487篇文献的全面检索，我们纳入89篇原创研究，总结出传统ROP诊断方法存在主观性强及需人工分析的局限性，常导致临床决策不一致。人工智能在改善ROP管理方面具有巨大潜力，本综述系统探讨了AI在ROP检测、分类、诊断及预后中的应用前景。