Retinopathy of prematurity (ROP) is a leading cause of preventable vision loss in preterm infants. While appropriate screening is crucial for early identification and treatment of ROP, current screening guidelines remain limited by inter-examiner variability in screening modalities, absence of local protocol for ROP screening in some settings, a paucity of resources and an increased survival of younger and smaller infants. This review summarizes the advancements and challenges of current innovative technologies, artificial intelligence (AI), and predictive biomarkers for the diagnosis and management of ROP. We provide a contemporary overview of AI-based models for detection of ROP, its severity, progression, and response to treatment. To address the transition from experimental settings to real-world clinical practice, challenges to the clinical implementation of AI for ROP are reviewed and potential solutions are proposed. The use of optical coherence tomography (OCT) and OCT angiography (OCTA) technology is also explored, providing evaluation of subclinical ROP characteristics that are often imperceptible on fundus examination. Furthermore, we explore several potential biomarkers to reduce the need for invasive procedures, to enhance diagnostic accuracy and treatment efficacy. Finally, we emphasize the need of a symbiotic integration of biologic and imaging biomarkers and AI in ROP screening, where the robustness of biomarkers in early disease detection is complemented by the predictive precision of AI algorithms.

早产儿视网膜病变 (ROP) 是早产儿可预防视力丧失的主要原因。虽然适当的筛查对于早期识别和治疗 ROP 至关重要，但目前的筛查指南仍然受到检查者间筛查方式的差异、某些情况下缺乏 ROP 筛查的当地方案、资源匮乏以及年轻人和体型较小者生存率增加等因素的限制。婴儿。本综述总结了当前创新技术、人工智能 (AI) 和预测性生物标志物在 ROP 诊断和管理方面的进展和挑战。我们提供了基于人工智能的 ROP 检测模型的当代概述、其严重程度、进展和治疗反应。为了解决从实验环境到现实临床实践的过渡，我们回顾了 ROP 人工智能临床实施面临的挑战，并提出了潜在的解决方案。还探索了光学相干断层扫描 (OCT) 和 OCT 血管造影 (OCTA) 技术的使用，以评估在眼底检查中通常难以察觉的亚临床 ROP 特征。此外，我们探索了几种潜在的生物标志物，以减少侵入性手术的需要，提高诊断准确性和治疗效果。最后，我们强调在 ROP 筛查中需要将生物和成像生物标志物与人工智能共生整合，其中生物标志物在早期疾病检测中的稳健性得到人工智能算法的预测精度的补充。