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

• 批准号: 10685346
• 负责人: Caitlin Dreisbach
• 金额: $ 15.88万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 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/10685346
• 关键词: 37 weeks gestation; Abdomen; Agreement; Algorithmic Analysis; Algorithms; Anatomy; Award; Biometry; Birth; Birth Certificates; Birth Weight; Blinded; Body mass index; Cardiology; Cesarean section; Characteristics; Clinical; Clinical assessments; Data; Data Science; Decision Making; Detection; Development; Diagnosis; Diameter; Discipline of Nursing; Discipline of obstetrics; Environment; Eye diseases; Femur; Fetal Weight; Fingers; Future; Gestational Age; Goals; Growth; Head circumference; Height; Hospitals; Image; Image Analysis; Infant; Infant Development; Information Systems; Interdisciplinary Study; Intervention; Learning; Length; Manuals; Maternal-fetal medicine; Measurement; Medicine; Mentored Research Scientist Development Award; Mentors; Mentorship; Methods; Midwife; National Institute of Nursing Research; Neural Network Simulation; Newborn Infant; Nurses; Nursing Assessment; Observational Study; Operative Surgical Procedures; Overweight; Palpation; Parietal; Participant; Patients; Pattern Recognition; 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; Uterus; Weight; Woman; Women' s Health; antenatal; breast density; 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; skill acquisition; skills; success; tool; tumor; ultrasound

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.

项目摘要和摘要 出于生长跟踪和 实验室计划是安全产前护理的关键组成部分。估计目前依赖超声衍生的 特定胎儿平面的测量，以间接评估生长和福祉。标准胎儿生物识别 估计胎儿体重的测量值（双直径，头圆，腹部 周长和股骨长度）与实际胎儿体重的相关性很差，定义为 出生时克的新生重量。对于出生时超过4,000克的新生儿，当前的错误估计值 妊娠三个月末期的胎儿体重仅准确40％。否 依靠胎儿生物特征测量的时间更长，数据科学方法具有估计胎儿的潜力 与标准回归方法相比，偏差和错误的重量较低。迄今为止，还没有使用研究 超声图像，不仅是胎儿测量值 重量。该提案的总体目标是发展领导必要的技能和培训 在怀孕期间使用数据科学用于临床评估的发展。使用现有的超声 来自罗切斯特大学（UR）医学医院的成像和出生证明数据（n = 17,478名患者） R01研究中的手指湖泊区域围产期/产科数据系统（PDS）和n = 310名患者 了解怀孕信号和婴儿发育（上行：R01HD083369），具体目的是：1） 确定孕产妇（即体重指数）和胎儿因素（即生长测量）的增加 hadlock公式对胎儿体重的估计与实际出生体重之间的不一致 使用来自PDS的出生证明数据的新生儿，2）评估CNN算法的准确性 与Hadlock公式相比，第三个孕期的超声图像估计胎儿体重，3） 在上升研究的新超声图像上测试有效性CNN算法。该提议将 利用Caitlin Dreisbach博士的专业知识，计算资源和 UR护理学院，Gorgen数据科学研究所和 罗切斯特技术学院。这项研究的结果有可能改变实践并改善 怀孕三个月晚期的临床评估。研究和修改培训 该奖项中包括，Drisbach博士确定了成为独立的长期职业目标 研究人员具有将数据科学转化为产科临床护理方面的专业知识。