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在怀孕期间使用视网膜血管特征进行非侵入性早期预测妊娠前症

Yuxuan Wu¹, Lixia Shen², Lanqin Zhao¹

¹State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.

NPJ digital medicine

|April 5, 2025

概括

一个新的AI模型,PROMPT,使用视网膜图像来预测怀孕早期产前 (PE) 风险. 这种非侵入性,具有成本效益的工具可以改善早期检测并降低孕产妇死亡率.

科学领域:

产科和妇科产科和妇科
人工智能在医学中的应用
眼科医生眼科眼科

背景情况:

孕前 (PE) 是孕产妇和新生儿死亡的主要原因之一.
目前的PE生物标志物具有侵入性,成本高,应用有限.
早期预测对于及时干预和改善结果至关重要.

研究的目的:

引入和验证PROMPT,这是一个人工智能驱动的模型,用于早期预产不良症预测.
通过视网膜摄影和临床数据来评估PROMPT的性能.
评估PROMPT的成本效益和临床效用.

主要方法:

开发了PROMPT模型 (预先的危险因子+眼科数据+平均动脉压预测测试).
使用深度学习从1812个怀孕14周前的视网膜照片中提取的视网膜参数.
机器学习用于预测PE和早产PE.

主要成果:

在预测PE时,PROMPT的AUC为0.87,在预测PE时为0.91.
显著超过基线模型的表现 (p < 0.001).
改善了从35%到41%的严重不良妊娠结果的检测,估计每10万次查将节省超过5000万美元的成本.

结论:

PROMPT提供了一种非侵入性,具有成本效益的方法,用于早期预测孕前.
由人工智能驱动的模型显示了改善产前护理的巨大潜力,特别是在资源有限的环境中.
PROMPT可以显著降低与PE相关的发病率和死亡率.

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