视网膜静脉封闭的多模式数据驱动方法:整合机器学习和生物信息学的叙述性综述
Chunlan Liang1, Lian Liu1, Jingxiang Zhong1
1Department of Ophthalmology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Advances in ophthalmology practice and research
|September 2, 2025
概括
机器学习和生物信息学整合正在彻底改变视网膜静脉封闭 (RVO) 研究. 数据驱动的策略和人工智能工具为早期诊断和个性化治疗提供了潜力,
科学领域:
- 眼科 眼科
- 医疗信息学
- 生物信息学
- 机器学习
背景情况:
- 视网膜静脉封闭 (RVO) 是整体视力损伤的主要原因.
- 病理生理学包括血管阻塞,缺血和炎症.
- 多模式数据集成有助于提高RVO的理解,诊断和治疗.
研究的目的:
- 审查机器学习,生物信息学和临床医学方面的最新进展.
- 探索RVO的预测模型,分子机制和生物标志物.
- 突出数据驱动的RVO研究和患者护理策略.
主要方法:
- 跨学科研究的批判性综合.
- 专注于机器学习和生物信息集成.
- 在视网膜成像和多omics网络深度学习的分析.
主要成果:
- 机器学习与生物信息学的整合增强了RVO预测建模和机械洞察力.
- 在视网膜成像和多omics网络应用的深度学习是有前途的.
- 挑战包括数据标准化和临床翻译的模型通用性.
结论:
- 多中心验证框架和可解释的人工智能对于临床翻译至关重要.
- 专注于患者的数据平台和跨学科的合作是必不可少的.
- 精确干预可以使RVO患者的视力得到最佳保护.
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