Neural Network Approach to Estimate Fetal Weight in the Late Third Trimester of Pregnancy

项目摘要和摘要 胎儿体重估计，或产前胎儿体重的评估，用于生长跟踪和 分娩计划是安全产前护理的关键组成部分。目前的评估依赖于超声得出的 测量特定的胎儿平面，以间接评估生长和健康状况。标准胎儿生物测量 估计胎儿体重的测量方法(双顶径、头围、腹围 周长和股骨长度)与实际胎儿体重的相关性很差，定义为 新生儿出生时的体重(克)。对于出生时体重在4000克以上的新生儿，目前的误差估计为 妊娠晚期胎儿体重的准确率只有40%左右。不是的 不再依赖胎儿生物测量，数据科学方法有可能估计胎儿 与标准回归方法相比，该方法具有更低的偏差和误差。到目前为止，还没有研究使用 超声图像，而不仅仅是胎儿的尺寸，作为神经网络方法的输入来估计胎儿 重量。这项提议的首要目标是发展必要的技能和培训 数据科学在孕期临床评估中的应用进展。利用现有的超声波 来自罗切斯特大学(UR)医学院的影像和出生证明数据(n=17,478名患者) 和指湖地区围产期/产科数据系统(PDS)，以及R01研究中的n=310名患者， 了解怀孕信号和婴儿发育(上图：R01HD083369)，具体目标为：1) 确定增加的母体因素(即体重指数)和胎儿因素(即生长测量) 用Hadlock公式估算的胎儿体重与新生儿实际出生体重的不一致性 使用来自PDS的出生证明数据的新生儿，2)评估CNN算法在 与Hadlock公式相比，在妊娠晚期进行超声成像以估计胎儿体重，以及3) 测试CNN算法在来自Upside研究的新超声图像上的有效性。这项提议将 利用Caitlin Dreisbach博士指导团队的专业知识、计算资源和 UR护理学院、戈尔根数据科学研究所和 罗切斯特理工学院。这项研究的结果有可能改变实践和改进 妊娠晚期的临床评估。研究性学习与辅导性培训 这一奖项允许德雷斯巴赫博士确立她成为独立人士的长期职业目标 在将数据科学转化为产科临床护理方面具有专业知识的调查员。

Project Summary and Abstract Fetal weight estimation, or the assessment of antenatal fetal weight for the purposes of growth tracking and labor planning, is a critical component of safe prenatal care. Estimations currently rely on ultrasound-derived measurements of specific fetal planes to indirectly assess growth and wellbeing. The standard fetal biometric measurements for the estimation of fetal weight (biparietal diameter, head circumference, abdominal circumference and femur length) are poorly correlated to actual fetal weight, defined as the measurement of newborn weight in grams at birth. For newborns who are above 4,000 grams at birth, current error estimates of fetal weight in the late-third trimester of pregnancy are only accurate approximately 40% of the time. By no longer relying on fetal biometric measurements, data science approaches have the potential to estimate fetal weight with lower bias and errors compared to standard regression methods. To date, no studies have used ultrasound images, not just the fetal measurements, as input into a neural network approach to estimate fetal weight. The overarching goal of this proposal is to develop the skills and training necessary to lead the advancement of data science for use in clinical assessment during pregnancy. Using existing ultrasound imaging and birth certificate data (n=17,478 patients) from the University of Rochester (UR) Medicine Hospitals and the Finger Lakes Regional Perinatal/Obstetrics Data System (PDS), and n= 310 patients in the R01 study, Understanding Pregnancy Signals and Infant Development (UPSIDE: R01HD083369), the specific aims are: 1) To determine the maternal (i.e., body mass index) and fetal factors (i.e., growth measurements) that increase the discordance between the estimation of fetal weight by the Hadlock formula and actual birth weight of neonates using birth certificate data from the PDS, 2) To evaluate the accuracy of a CNN algorithm on ultrasound images in the third trimester to estimate fetal weight compared to the Hadlock formula, and 3) To test the effectiveness CNN algorithm on new ultrasound images from the UPSIDE study. This proposal will leverage the expertise of Dr. Caitlin Dreisbach’s mentorship team, computational resources, and the exceptional research environment at the UR School of Nursing, Goergen Institute for Data Science, and the Rochester Institute of Technology. Results from this study have the potential to change practice and improve clinical assessments during the late third trimester of pregnancy. The research study and mentored training included in this award allows Dr. Dreisbach to establish her long-term career goal of becoming an independent investigator with expertise in the translation of data science to obstetric clinical care.