Liang Chunlan, Liu Lian, Zhong Jingxiang
Department of Ophthalmology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Adv Ophthalmol Pract Res. 2025 Jul 14;5(4):235-244. doi: 10.1016/j.aopr.2025.07.002. eCollection 2025 Nov-Dec.
Retinal vein occlusion (RVO) is a leading cause of visual impairment on a global scale. Its pathological mechanisms involve a complex interplay of vascular obstruction, ischemia, and secondary inflammatory responses. Recent interdisciplinary advances, underpinned by the integration of multimodal data, have established a new paradigm for unraveling the pathophysiological mechanisms of RVO, enabling early diagnosis and personalized treatment strategies.
This review critically synthesizes recent progress at the intersection of machine learning, bioinformatics, and clinical medicine, focusing on developing predictive models and deep analysis, exploring molecular mechanisms, and identifying markers associated with RVO. By bridging technological innovation with clinical needs, this review underscores the potential of data-driven strategies to advance RVO research and optimize patient care.
Machine learning-bioinformatics integration has revolutionised RVO research through predictive modelling and mechanistic insights, particularly via deep learning-enhanced retinal imaging and multi-omics networks. Despite progress, clinical translation requires resolving data standardisation inconsistencies and model generalizability limitations. Establishing multicentre validation frameworks and interpretable AI tools, coupled with patient-focused data platforms through cross-disciplinary collaboration, could enable precision interventions to optimally preserve vision.
视网膜静脉阻塞（RVO）是全球范围内导致视力损害的主要原因。其病理机制涉及血管阻塞、缺血和继发性炎症反应的复杂相互作用。最近，在多模态数据整合的支持下，跨学科进展为揭示RVO的病理生理机制建立了新的范例，从而实现早期诊断和个性化治疗策略。
本综述批判性地综合了机器学习、生物信息学和临床医学交叉领域的最新进展，重点在于开发预测模型和深度分析、探索分子机制以及识别与RVO相关的标志物。通过将技术创新与临床需求相结合，本综述强调了数据驱动策略在推进RVO研究和优化患者护理方面的潜力。
机器学习与生物信息学的整合通过预测建模和机制洞察，特别是通过深度学习增强的视网膜成像和多组学网络，彻底改变了RVO研究。尽管取得了进展，但临床转化需要解决数据标准化不一致和模型可推广性的局限性。通过跨学科合作建立多中心验证框架和可解释的人工智能工具，以及以患者为中心的数据平台，可能实现精准干预，以最佳地保护视力。
