Laboratorio de Imágenes Médicas, Departamento de Ingeniería, Pontificia Universidad Católica del Perú, Lima, Peru.
Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, United States of America.
PLoS One. 2022 Feb 9;17(2):e0262107. doi: 10.1371/journal.pone.0262107. eCollection 2022.
Ultrasound imaging is a vital component of high-quality Obstetric care. In rural and under-resourced communities, the scarcity of ultrasound imaging results in a considerable gap in the healthcare of pregnant mothers. To increase access to ultrasound in these communities, we developed a new automated diagnostic framework operated without an experienced sonographer or interpreting provider for assessment of fetal biometric measurements, fetal presentation, and placental position. This approach involves the use of a standardized volume sweep imaging (VSI) protocol based solely on external body landmarks to obtain imaging without an experienced sonographer and application of a deep learning algorithm (U-Net) for diagnostic assessment without a radiologist. Obstetric VSI ultrasound examinations were performed in Peru by an ultrasound operator with no previous ultrasound experience who underwent 8 hours of training on a standard protocol. The U-Net was trained to automatically segment the fetal head and placental location from the VSI ultrasound acquisitions to subsequently evaluate fetal biometry, fetal presentation, and placental position. In comparison to diagnostic interpretation of VSI acquisitions by a specialist, the U-Net model showed 100% agreement for fetal presentation (Cohen's κ 1 (p<0.0001)) and 76.7% agreement for placental location (Cohen's κ 0.59 (p<0.0001)). This corresponded to 100% sensitivity and specificity for fetal presentation and 87.5% sensitivity and 85.7% specificity for anterior placental location. The method also achieved a low relative error of 5.6% for biparietal diameter and 7.9% for head circumference. Biometry measurements corresponded to estimated gestational age within 2 weeks of those assigned by standard of care examination with up to 89% accuracy. This system could be deployed in rural and underserved areas to provide vital information about a pregnancy without a trained sonographer or interpreting provider. The resulting increased access to ultrasound imaging and diagnosis could improve disparities in healthcare delivery in under-resourced areas.
超声成像是高质量产科护理的重要组成部分。在农村和资源匮乏的社区，由于缺乏超声成像，导致孕妇的医疗保健存在相当大的差距。为了增加这些社区获得超声的机会，我们开发了一种新的自动化诊断框架，该框架无需有经验的超声技师或解释提供者即可进行胎儿生物测量、胎儿体位和胎盘位置的评估。这种方法涉及使用基于外部身体标志的标准化容积扫描成像（VSI）协议来获取图像，而无需有经验的超声技师，并且应用深度学习算法（U-Net）在没有放射科医生的情况下进行诊断评估。在秘鲁，一名没有超声经验的超声操作员按照标准协议接受了 8 小时的培训，进行了产科 VSI 超声检查。U-Net 经过训练可从 VSI 超声采集自动分割胎儿头部和胎盘位置，以随后评估胎儿生物测量、胎儿体位和胎盘位置。与专家对 VSI 采集的诊断解释相比，U-Net 模型在胎儿体位方面的一致性为 100%（Cohen's κ 1（p<0.0001）），在胎盘位置方面的一致性为 76.7%（Cohen's κ 0.59（p<0.0001））。这对应于胎儿体位的 100%敏感性和特异性，以及前位胎盘位置的 87.5%敏感性和 85.7%特异性。该方法还实现了双顶径和头围的相对误差低至 5.6%和 7.9%。生物测量值与标准护理检查分配的估计胎龄相差 2 周以内，准确率高达 89%。该系统可以部署在农村和服务不足的地区，无需经过培训的超声技师或解释提供者即可提供有关妊娠的重要信息。增加获得超声成像和诊断的机会可以改善资源匮乏地区医疗服务的差异。
