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人工智能和产后出血问题

Sam J Mathewlynn1,2, Mohammadreza Soltaninejad1,3, Sally L Collins1

  • 1Nuffield Department of Women's and Reproductive Health, University of Oxford, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DU, United Kingdom.

Maternal-fetal medicine (Wolters Kluwer Health, Inc.)
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概括
此摘要是机器生成的。

人工智能 (AI) 在预测产后出血 (PPH) 方面表现有前途. 然而,需要更多的研究来验证人工智能模型,并确保它们在各种全球医疗保健环境中的适用性,特别是在资源不足的地区.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.机器学习 机器学习产后出血 产后出血 产后出血风险预测风险预测

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 孕产妇健康 孕产妇健康

背景情况:

  • 产后出血 (PPH) 是全球孕产妇死亡的主要原因.
  • 尽管取得了进展,但PPH仍然是一个重大挑战,即使在发达国家.
  • 人工智能 (AI) 越来越多地被用于医疗保健应用.

研究的目的:

  • 探索AI在预测和管理产后出血 (PPH) 的应用.
  • 审查目前用于PPH风险分层的AI模型,并确定局限性.
  • 突出在PPH管理中对AI的未来研究方向.

主要方法:

  • 审查现有关于人工智能应用在PPH预测和管理方面的研究.
  • 对人工智能模型性能,验证挑战和在不同环境中的适用性进行分析.
  • 探索新的AI方法,包括子宫收缩和放射学.

主要成果:

  • 一些AI模型在预测PPH方面显示出有希望的结果,但往往缺乏外部验证.
  • 目前的研究主要来自资源丰富的环境,对资源有限的地区的模型有限.
  • 人工智能也在通过可穿戴设备进行血液产品管理和早期检测.

结论:

  • 人工智能为改善PPH预测和管理提供了巨大的潜力.
  • 关键的挑战包括模型验证,临床翻译和确保在各种医疗保健系统中的适用性.
  • 进一步的研究,特别是在低收入和中等收入国家,对于利用人工智能的全部潜力来实现全球PPH减少至关重要.