Evidence map›Paper›PMID 41824048›Full record

ArticlePediatric radiology2026

Deep learning based gestational age estimation from multi-view fetal brain magnetic resonance imaging.

Shuai Luo, Meng Liu, Nian-Zu Lv, Guo-Wei Dai, Kai-Jun Ma, Meng Jun Zhan, Yu-Xiao Sun, Hui-Kun Yang, Zhen-Hua Deng, Yuan-He Wang and 2 more

Abstract read
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Article in Pediatric radiology, 2026. 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

12 authors.

Shuai LuoCollege of Computer Science, Sichuan University, Chengdu, China, 610065, Sichuan.
Meng LiuCollege of Computer Science, Sichuan University, Chengdu, China, 610065, Sichuan.
Nian-Zu LvCollege of Computer Science, Sichuan University, Chengdu, China, 610065, Sichuan.
Guo-Wei DaiCollege of Computer Science, Sichuan University, Chengdu, China, 610065, Sichuan.
Kai-Jun MaShanghai Key Laboratory of Crime Scene Evidence, ShangHai, China, 200083, ShangHai.
Meng Jun ZhanDepartment of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China, 610041, Sichuan.
Yu-Xiao SunDepartment of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China, 610041, Sichuan.
Hui-Kun YangDepartment of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China, 610041, Sichuan.
Zhen-Hua DengDepartment of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China, 610041, Sichuan.
Yuan-He WangDepartment of Forensic Medicine, Guizhou Medical University, Guiyang, China, 550004, Guizhou. yuanhewang@sohu.com.
Hu ChenCollege of Computer Science, Sichuan University, Chengdu, China, 610065, Sichuan. huchen@scu.edu.cn.
Fei FanDepartment of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China, 610041, Sichuan. fanfei@scu.edu.cn.

Funding

National Natural Science Foundation of China 82402197National Natural Science Foundation of China 82471915Natural Science Foundation of Sichuan Province 2024NSFSC1517Opening Project of Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education 2024KFKT02Opening Project of Shanghai Key Laboratory of Crime Scene Evidence 2023XCWZK03
6 · The paper itself

Abstract

backgroundGestational age (GA) is essential for assessing fetal development, but conventional methods such as last menstrual period and ultrasound are often inaccurate, particularly in late pregnancy. Recent advances in deep learning (DL) and MRI offer more reliable and consistent GA estimation by capturing detailed fetal brain development.

objectiveThis study aimed to develop deep learning models for GA prediction using multi-view fetal brain MRI and to compare their performance with conventional biometric regression techniques. MATERIALS AND

methodsA total of 1,321 fetal MRI scans were used to train and evaluate various DL models, while an additional 80 publicly available MRI scans served as an external test set. Two training strategies were explored: transfer learning versus training from scratch, and single-view versus multi-modality input.

resultsThe pre-trained ResNet-101 model achieved a mean absolute error (MAE) of 4.47 days and a coefficient of determination (R

conclusionsThe integration of multi-view MRI and transfer learning significantly enhanced the predictive accuracy of DL models for GA estimation. The proposed approach outperformed conventional biometric regression and highlighted clinically relevant anatomical regions, demonstrating its potential for use in prenatal diagnostic applications.

Indexed as

BrainDeep LearningGestational AgeMagnetic Resonance ImagingPrenatal DiagnosisConvolutional Neural NetworksFemaleHumansPregnancyConvolutional neural networksDeep learningFetal brain age estimationMulti-view magnetic resonance imaging

Identifiers

PMID41824048

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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.