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: 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
• 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' 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

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.

项目摘要及摘要