论文标题
胎儿超声约会扫描中的临床方案依从性的深度学习质量评估
Deep Learning-based Quality Assessment of Clinical Protocol Adherence in Fetal Ultrasound Dating Scans
论文作者
论文摘要
为了评估怀孕期间的胎儿健康，医生使用基于牙冠臀部长度（CRL）测量的胎龄（GA）计算，以检查胎儿的大小和生长轨迹。但是，基于CRL的GA估计需要在胎冠和臀部视图上正确定位卡尺，这并不总是很容易找到的平面，尤其是对于缺乏经验的超声仪而言。从真实的CRL视图中找到稍微倾斜的视图可能会导致不同的CRL值，因此GA的估计不正确。这项研究通过验证7个用于验证获得平面的正确性的临床评分标准，提出了一种基于AI的方法，用于对CRL视图进行质量评估。与专家相比，我们展示了我们提议的解决方案如何在大多数评分标准上实现高精度。我们还表明，如果使用了这种评分系统，它有助于准确识别出不良获取的图像，因此可以帮助超声检查员获得更好的图像，这可能会导致对诸如宫内内生长限制（IUGR）等状况的更好评估。
To assess fetal health during pregnancy, doctors use the gestational age (GA) calculation based on the Crown Rump Length (CRL) measurement in order to check for fetal size and growth trajectory. However, GA estimation based on CRL, requires proper positioning of calipers on the fetal crown and rump view, which is not always an easy plane to find, especially for an inexperienced sonographer. Finding a slightly oblique view from the true CRL view could lead to a different CRL value and therefore incorrect estimation of GA. This study presents an AI-based method for a quality assessment of the CRL view by verifying 7 clinical scoring criteria that are used to verify the correctness of the acquired plane. We show how our proposed solution achieves high accuracy on the majority of the scoring criteria when compared to an expert. We also show that if such scoring system is used, it helps identify poorly acquired images accurately and hence may help sonographers acquire better images which could potentially lead to a better assessment of conditions such as Intrauterine Growth Restriction (IUGR).
