Evidence map›Paper›PMID 41387790›Full record

ArticleBMC pregnancy and childbirth2025

A prior knowledge-guided convolutional neural network model for predicting fetal brain age from MRI during second and third trimesters.

Fangmei Zhu, Shijie Huang, Xue Tang, Kai Zhang, Jian Wang, Dinggang Shen, Zhongxiang Ding

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In one paragraph

Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Fangmei Zhu *Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No.261, Huansha Road, Hangzhou, Zhejiang, 310006, China.
Shijie Huang *School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, Shanghai Tech University, Shanghai, 201210, China.
Xue TangSchool of Medical Imaging, Hangzhou Medical College, Hangzhou, 311399, Zhejiang, China.
Kai ZhangSchool of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, Shanghai Tech University, Shanghai, 201210, China.
Jian WangDepartment of Radiology, Tongde Hospital of Zhejiang Province 310012, Hangzhou, Zhejiang, China.
Dinggang ShenSchool of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, Shanghai Tech University, Shanghai, 201210, China. dgshen@shanghaitech.edu.cn.
Zhongxiang DingDepartment of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No.261, Huansha Road, Hangzhou, Zhejiang, 310006, China. hangzhoudzx73@126.com.

Funding

Zhejiang Medical Health Science and Technology Planning Program No. 2024KY874Zhejiang Traditional Chinese Medical Science and Technology Planning Program No. 2024ZR062
6 · The paper itself

Abstract

backgroundThis study aimed to evaluate the efficacy of a convolutional neural network (CNN) model in estimating fetal brain age from MRI scans during second and third trimesters.

methodsA total of 407 T2-weighted MRI including 2310 stacks in the axial, sagittal, and coronal planes were used. The average fetal brain age was 30.7 weeks (ranging from 22 to 39 weeks). The reference standard gestational age was based on ultrasonography measurements during the first trimester. A prior knowledge-guided CNN model was utilized for brain age prediction. The relationship between the predicted brain age and the reference standard was assessed using Lin's concordance correlation coefficient (ρc) and determination coefficient (R

resultsThe model exhibited a MAE of 4.62 ± 3.31 days, aligning closely with the reference standard (ρc = 0.977) and demonstrating a robust correlation between predicted and actual brain age (R

conclusionA prior knowledge-guided CNN model that utilized fetal brain MRI data from the second and third trimesters can accurately predict fetal brain age, which holds significant potential for optimizing pregnancy management.

Indexed as

BrainMagnetic Resonance ImagingNeural Networks, ComputerAdultConvolutional Neural NetworksFemaleGestational AgeHumansPregnancyPregnancy Trimester, SecondPregnancy Trimester, ThirdUltrasonography, PrenatalBrain ageConvolutional neural networkFetusMRI

Identifiers

PMID41387790
PMCPMC12699803

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.