全身胎儿MRI和胎儿体重估计的基于深度学习的细分:评估性能,可重复性和可重复性
Bella Specktor-Fadida1, Daphna Link-Sourani2, Aviad Rabinowich2,3,4
1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. bella.specktor@mail.huji.ac.il.
European radiology
|September 2, 2023
概括
使用MRI估计胎儿体重的新人工智能方法在检测生长限制方面显示出高准确度和灵敏度. 这种工具有助于评估胎儿发育和识别有风险的怀孕.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胎儿的发育 胎儿的发育
背景情况:
- 准确的胎儿体重估计对于监测妊娠健康至关重要.
- 磁共振成像 (MRI) 提供了详细的解剖学可视化.
- 深度学习为产科中高级图像分析提供了机会.
研究的目的:
- 开发和验证使用MRI进行全身胎儿细分的深度学习模型.
- 建立基于MRI的正常胎儿体重增长图.
- 评估模型检测有生长限制 (FGR) 的胎儿的能力.
主要方法:
- 分析了348个胎儿 (19-39周 GA) 的回顾性队列.
- 为胎儿细分开发了一种具有质量估计模块的深度学习模型.
- 该模型的可重复性,可复制性和准确性被严格地根据黄金标准评估.
主要成果:
- 细分模型实现了高性能 (子=0.973) 和与出生体重的良好一致.
- 重复性和可重复性很高,估计胎儿体重 (EFW) 的平均差异很小.
- 基于MRI的EFW准确地识别了生长限制的胎儿,包括在第三百分点以下的胎儿.
结论:
- 为EFW开发的基于AI的MRI方法是准确的,可重复的和可重复的.
- 一个MRI衍生的胎儿体重增长图为正常发育提供了有价值的参考.
- 这种方法显示出识别有生长限制风险的胎儿的巨大潜力.
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