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

神经网络方法估计妊娠晚期胎儿体重

基本信息

批准号：
10507172
负责人：
Caitlin Dreisbach
金额：
$ 15.87万
依托单位：
UNIVERSITY OF ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-17 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10507172
关键词：
37 weeks gestation Abdomen Agreement Algorithmic Analysis Algorithms Anatomy Award Biometry Birth Birth Certificates Birth Weight Blinded Body mass index Breast Caliber Cardiology Cesarean section Characteristics Clinical Clinical assessments Data Data Science Decision Making Development Diagnosis Discipline of Nursing Discipline of obstetrics Environment Eye Femur Fetal Weight Fibrinogen Fingers Future Gestational Age Goals Growth Head circumference Height Hospitals Image Image Analysis Infant Infant Development Information Systems Institutes Interdisciplinary Study Intervention Lead Learning Length Manuals Maternal-fetal medicine Measurement Medicine Mentored Research Scientist Development Award Mentors Mentorship Methods National Institute of Nursing Research Neural Network Simulation Newborn Infant Nurse Midwives Nursing Assessment Observational Study Operative Surgical Procedures Overweight Palpation Participant Patients Pattern Perinatal Personal Satisfaction Physical Examination Physicians Policies Practice Guidelines Pregnancy Pregnant Women Prenatal care Provider Radiology Specialty Research Research Personnel Role School Nursing Signal Transduction Specificity Technology Third Pregnancy Trimester Time Training Translations Ultrasonography United States Universities Weight Woman Women&apos s Health antenatal base career career development clinical care clinical decision-making computer science computing resources convolutional neural network effectiveness testing fetal improved multimodality neonate neural network neural network algorithm neurodevelopment policy implication prospective research study skills training success tool tumor ultrasound

项目摘要

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.

项目概要和摘要胎儿体重估计，或出于生长跟踪和评估目的的产前胎儿体重评估分娩计划是安全产前护理的重要组成部分，目前依赖于超声波。对特定胎儿平面的测量，以间接评估胎儿的生长和健康状况。用于估计胎儿体重的测量（双顶径、头围、腹围）周长和股骨长度）与实际胎儿体重（定义为测量新生儿出生体重（以克为单位）对于出生时体重超过 4,000 克的新生儿，目前的误差估计为妊娠晚期胎儿体重的准确率约为 40%。不再依赖胎儿生物识别测量，数据科学方法有可能估计胎儿迄今为止，还没有研究使用过与标准回归方法相比具有较低偏差和误差的权重。超声图像，而不仅仅是胎儿测量值，作为神经网络方法的输入来估计胎儿该提案的总体目标是培养领导所需的技能和培训。使用现有超声波进行妊娠期临床评估的数据科学的进步。来自罗切斯特大学 (UR) 医学医院的影像和出生证明数据（n=17,478 名患者）和 Finger Lakes 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（UPSIDE：R01HD083369），具体目标是：1）确定增加体重的母体因素（即体重指数）和胎儿因素（即生长测量值） Hadlock 公式估算的胎儿体重与实际出生体重之间的不一致新生儿使用 PDS 的出生证明数据，2) 评估 CNN 算法的准确性与 Hadlock 公式相比，使用妊娠晚期的超声图像来估计胎儿体重，以及 3) 该提案将在 UPSIDE 研究的新超声图像上测试 CNN 算法的有效性。利用 Caitlin Dreisbach 博士导师团队的专业知识、计算资源和 UR 护理学院、格尔根数据科学研究所和罗彻斯特理工学院的这项研究结果有可能改变实践并改进。妊娠晚期的临床评估和指导培训。纳入该奖项使 Dreisbach 博士能够确立成为独立的长期职业目标具有将数据科学转化为产科临床护理的专业知识的研究者。