Asset Details
Do you wish to reserve the book?
Attention-guided deep learning for gestational age prediction using fetal brain MRI
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Attention-guided deep learning for gestational age prediction using fetal brain MRI
Do you wish to request the book?
Attention-guided deep learning for gestational age prediction using fetal brain MRI
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Attention-guided deep learning for gestational age prediction using fetal brain MRI
2022
Overview
Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R
2
score of 0.945, mean absolute error of 6.7 days, and concordance correlation coefficient of 0.970. The convolutional neural network was trained on a heterogeneous dataset of 741 developmentally normal fetal brain images ranging from 19 to 39 weeks in gestational age. We also demonstrate model performance and generalizability using independent datasets from four academic institutions across the U.S. and Turkey with R
2
scores of 0.81–0.90 after minimal fine-tuning. The proposed regression algorithm provides an automated machine-enabled tool with the potential to better characterize in utero neurodevelopment and guide real-time gestational age estimation after the first trimester.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ Age
/ Brain - growth & development
/ Female
/ Fetus
/ Fetuses
/ Humanities and Social Sciences
/ Humans
/ Image Processing, Computer-Assisted - statistics & numerical data
/ Magnetic Resonance Imaging - methods
/ Magnetic Resonance Imaging - standards
/ Pregnancy Trimesters - physiology
/ Science
/ Turkey
