Multimodal data-driven approaches in retinal vein occlusion: A narrative review integrating machine learning and bioinformatics

Background

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.

Main text

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.

Conclusions

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.