"水缩症严重性指数"指导了儿童产前水缩症的管理,该指数基于人工智能,仅应用于超声波图像
Lauren Erdman1,2,3,4,5,6, Mandy Rickard7, Erik Drysdale8
1Division of Genetics and Genome Biology, Hospital for Sick Children Research Institute, Toronto, ON, USA. lauren.erdman@cchmc.org.
Scientific reports
|September 30, 2024
概括
一个新的人工智能系统,水裂严重程度指数 (HSI),准确地预测从产前超声波的外科手术需求. 这种工具有助于对患者进行分类,确保对那些需要手术的患者及时进行干预,同时有效监测其他患者.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 儿科泌尿外科 儿科泌尿外科
背景情况:
- 产前水 (HN) 影响高达5%的怀孕.
- 严密监测对于手术干预决策至关重要.
- 当前的方法可能是资源密集的.
研究的目的:
- 使用深度学习开发一个自动化的水缩严重程度指数 (HSI).
- 从脏超声波图像直接预测手术干预的需要.
- 引导临床决策在产前水缩管理.
主要方法:
- 一个深度学习模型被应用到儿科脏超声波图像.
- 该模型在四个北美儿科医院进行了培训和验证.
- 使用AUROC,AUROC,灵敏度和特异性来评估性能.
主要成果:
- 在内部测试中,HSI实现了>90%的AUROC和>90%的灵敏度.
- 外部验证显示AUROC≥90%和灵敏度≥80%.
- 该系统可靠地将患者分为低风险和高风险类别.
结论:
- 自动化HN严重性评估是从单个超声波可行的.
- HSI有效地分层风险,有助于患者分类.
- 这种人工智能系统提供了一种可靠的方法来预测HN的外科干预需求.
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