人工智能可以确定胎儿的性别
Emily H Frisch1, Anant Jain2, Mike Jin2
1Women's Health Institute, Cleveland Clinic, Cleveland, Ohio.
American journal of perinatology
|February 9, 2024
概括
这项研究引入了一种新的人工智能 (AI) 模型,用于从超声波图像中确定胎儿的性别,从而实现高精度. 这种人工智能工具显示了改善产科护理的潜力,尤其是在超声波专业知识有限的地方.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 产科和妇科 产科和妇科
背景情况:
- 胎儿的性别确定对于生殖健康和遗传咨询至关重要.
- 目前的方法依赖于熟练的超声波操作员,在某些地区限制了可访问性.
- 人工智能 (AI) 的进步为医疗图像的自动分析提供了潜力.
研究的目的:
- 评估概念验证人工智能模型在精确地从超声波图像中确定胎儿性别方面的性能.
- 评估人工智能驱动的胎儿性别识别的信心和准确性.
- 探索人工智能整合到常规产科护理中的潜力.
主要方法:
- 使用EfficientNetB4架构的计算机视觉模型在11,769个超声波图像片上受过训练.
- 该模型在一个单独的数据集上进行了测试,该数据集包括来自大量实践的7443张图像片.
- 使用准确度,科恩的卡帕和曲线下的接收器操作特征区域 (ROC AUC) 评估了性能.
主要成果:
- 人工智能模型在测试组中获得了88.27%的准确率和0.843的科恩卡帕得分.
- 对于男性,女性和无法评估类别的ROC AUC分数分别为0.896,0.897和0.916.
- 在"添加文本"类别中,获得了0.981的高ROC AUC得分.
结论:
- 这种新型的人工智能模型展示了从超声数据中确定胎儿性别的高能力.
- 这些发现支持将人工智能集成到产科工作流程中,提高诊断能力.
- 这项技术可以显著有利于那些有限地获得专业超声波专业知识的地区.
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