孕前及其预测:传统与当代预测方法对比
1Department of Gynecology, China Aerospace Science & Industry Corporation 731 Hospital, Beijing, China.
概括
预测孕前 (PE) 对母亲健康至关重要. 人工智能 (AI) 深度学习 (DL) 模型的准确性更高,约为70%,与预测PE的传统方法相比,特别是晚发病例.
科学领域:
- 产科和妇科 产科和妇科
- 医疗信息学 医疗信息学
- 生物医学工程 生物医学工程
背景情况:
- 孕前 (PE) 是对孕产妇和产周健康的重大威胁.
- 早期预测,预防和管理对于减轻不良妊娠结果至关重要.
- 了解PE的流行病学,病因学,病理生理学和风险因素至关重要.
研究的目的:
- 审查PE的流行病学,病因学,病理生理学和风险因素.
- 讨论人工智能 (AI) 深度学习 (DL) 在PE预测中的新兴作用.
- 推进早期PE预测方法的理解和临床应用.
主要方法:
- 进行了叙事审查.
- 检查了传统的PE预测模型.
- 评估了用于PE预测的AI深度学习技术.
主要成果:
- 早期发育涉及诸如子宫动脉重塑不良,免疫反应,内皮功能障碍和血管生成不平衡等危险因素.
- 传统的PE预测模型的灵敏度和特异性有限 (30-50%的检测率).
- 人工智能模型达到大约70%的检测率,显示出对晚发性PE的优越预测能力.
结论:
- 人工智能深度学习技术在革命性PE预测和管理方面显示出前景.
- 基于人工智能的方法解决了有效风险评估传统模型的缺陷.
- 进一步的研究应该集中在扩大数据库和验证AI在不同人群中的表现.
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