远见的人工智能:通过视网膜成像和人工智能解码妊娠期间的全身血管健康和高血压疾病
Srilaxmi Bearelly1,2, Cassandra Areff3, Matthew K Hoffman4
1Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA.
medRxiv : the preprint server for health sciences
|December 8, 2025
概括
远见人工智能使用视网膜成像和人工智能来预测怀孕早期的高血压疾病. 这种方法提供了一个有生物学基础的,可解释的诊断工具,用于改善孕产妇和胎儿健康结果.
科学领域:
- 生殖医学 生殖医学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 妊娠高血压障碍 (HDP) 存在重大风险,包括孕产妇死亡率和长期心血管问题.
- 目前对HDP的选方法不精确,具有挑战性,需要更早,更准确地检测.
- 早期的胎盘功能障碍和全身内皮干扰与HDP有关,但潜在的机制需要进一步阐明.
研究的目的:
- 开发和验证一个人工智能驱动的平台,视觉人工智能,用于使用视网膜成像早期预测HDP.
- 调查超广场视网膜成像和血管建模的实用性,以识别HDP特定的微血管特征.
- 通过视网膜血管分析,提供对不同HDP亚型的病理生理学的机制性见解.
主要方法:
- 视觉人工智能集成200度超广场视网膜成像与生物接地血管建模.
- 该平台构建了母亲视网膜血管结构的可解释,基于图形的表示.
- 应用拓和几何分析来确定潜在的多民族队列 (n=1,267) 中特定条件的微血管特征.
主要成果:
- 远见人工智能显示了预孕前 (AUC=0.90),早期预孕前 (AUC=0.93) 和严重预孕前 (AUC=0.89) 的高预测性能.
- 人工智能模型还准确地预测了妊娠高血压 (AUC=0.91) 和慢性高血压 (AUC=0.90).
- 视网膜血管分析揭示了与不同HDP亚型相关的明显重塑模式,提供了机械洞察力.
结论:
- 母亲的视网膜作为一个微创生物传感器,用于早期系统性血管健康评估.
- 远见人工智能为早期HDP检测提供了一个临床可行的,生物基础的诊断框架.
- 这个人工智能平台显示了在改善HDP查和管理方面具有广泛的全球可扩展性的潜力.
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