眼睛是大数据时代进入心血管疾病的窗口
Yarn Kit Chan1, Ching-Yu Cheng1,2,3,4, Charumathi Sabanayagam1,2
1Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore.
深度学习算法 (DLA) 分析眼睛图像,以预测心血管疾病 (CVD) 风险因素和结果. 这篇评论探讨了DLAs.
科学领域:
- 心脏病学 心脏病学
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡和残疾原因.
- 眼睛图像包含与心血管疾病进展相关的丰富信息.
- 早期诊断和风险分层对于管理心血管疾病至关重要.
研究的目的:
- 审查最近的深度学习算法 (DLAs) 应用于眼睛图像的心血管疾病结果.
- 确定这些DLAs临床部署的挑战和未来方向.
- 探索正在勃发展的心脏眼科学交叉点.
主要方法:
- 对17个最近的DLA使用眼镜图像进行心血管疾病预测的系统审查.
- 对DLAs预测心血管风险因素和疾病结果的能力的分析.
- 评估当前临床实施的挑战和要求.
主要成果:
- DLAs通过视网膜底部照片准确预测各种心血管风险因素 (例如高血压) 和结果 (例如中风,心肌梗塞).
- DLA在预测慢性病和血液学疾病等相关疾病方面表现出显著的准确性.
- 该领域看起来很有前途,有可能从眼睛数据中预测年龄,性别和生活方式因素.
结论:
- 应用到眼部成像的深度学习显示了早期心血管疾病检测和风险评估的重大前景.
- 需要进一步的研究,以提高可通用性,外部验证和基准测试,以便在临床上广泛采用.
- 心脏眼科领域正在迅速发展,在心血管医疗保健中提供了个性化精密医学的潜力.
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