开发和验证人工智能辅助的产前超声波查系统,用于实习生
Ting Lei1, Jie Ling Feng1, Mei Fang Lin1
1Department of Ultrasonic Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
概括
一个人工智能系统通过实时评估超声波图像质量来帮助查胎儿异常. 这种人工智能工具可以帮助学员识别标准视图和结构,提高诊断准确度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 产科和妇科 产科和妇科
背景情况:
- 使用超声波进行胎儿异常查,对于缺乏经验的操作人员来说,由于捕捉和解释标准解剖视图的复杂性,这就构成了挑战.
- 图像质量的实时评估和胎儿结构的识别对于准确的产前查至关重要.
研究的目的:
- 开发和验证产前查人工智能系统 (PSAIS) 以实时评估胎儿超声波图像质量.
- 为了使系统能够在胎儿图像中指示关键解剖结构的存在或缺失.
主要方法:
- 使用YOLOv3架构开发了一个PSAIS,用于134,696张来自18-32周妊娠期胎儿的静态超声波图像.
- 采用全球和本地特征提取用于图像质量评估和结构识别.
- 在四家医院的836个真实世界胎儿扫描视频上验证了系统的性能,评估了19个标准视图的识别.
主要成果:
- 在0.943 (内部) 和0.958 (外部) 的接收器运行特征曲线下,通过多类宏观平均面积实现了高性能.
- 在ROC曲线下的微平均面积达到0.974 (内部) 和0.973 (外部).
- 在90.9%的类似标准视图中,PSAIS准确标记了关键结构,并正确表示缺失的结构.
结论:
- 开发的人工智能系统有效地协助实习生进行胎儿异常查.
- 在确定标准超声波视图和结构方面,PSAIS显示了与专家评估的高度一致.
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