AccScience Publishing / IJB / Online First / DOI: 10.36922/IJB025200192
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RESEARCH ARTICLE

Rapid 3D reconstruction in fetal ultrasound imaging using artificial intelligence and medical 3D printing

Wenjuan Zhang1† ,  Jiahe Liang2,3,4† ,  Linbin Lai1† ,  Zewen Zhang1 ,  Yitong Guo2† ,  Na Hou2† ,  Zekai Zhang2 ,  Zhuojun Mao2 ,  Tiesheng Cao2 ,  Yu Li5 ,  Lijun Yuan2* ,  Airong Qian1*
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1 Xi’an Key Laboratory of Special Medicine and Health Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, Shaanxi, China
2 Department of Ultrasound Diagnosis, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
3 State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
4 NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi’an Jiaotong University, Xi’an, Shaanxi, China
5 Xi’an Key Laboratory of Stem Cell and Regenerative Medicine, Institute of Medical Research, Northwestern Polytechnical University, Xi’an, Shaanxi, China
†These authors contributed equally to this work.
IJB, 025200192 https://doi.org/10.36922/IJB025200192
Received: 12 May 2025 | Accepted: 1 June 2025 | Published online: 3 June 2025
(This article belongs to the Special Issue 3D-Printed Biomedical Devices)
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Congenital heart disease (CHD) has been one of the most serious problems in newborns. For fetal heart health care, 3D modeling and printing technology have been adopted in the diagnosis of CHD during antenatal care. However, the development of 3D printing techniques and their clinical applications have been hindered by the manual processing of ultrasound (US) volume data in clinical practice. To overcome this problem, we present an interactive semi-automatic method based on deep learning that uses manual processing results from expert sonographers for training. The accuracy, interpretability, and variability of the performances were evaluated on the validation set. The results demonstrated that compared with a physician with less than 3 years of experience, a better Faster- region-based convolutional neural network-based threshold was achieved using our proposed fetal heart reconstruction technique (FRT), with enhanced performance based on the outflow tract view and three-vessel view. No significant difference was found among the clinical parameters, in proportion, measured from the model rebuilt using FRT and US volume data. Furthermore, the reconstruction time of the fetal heart blood pool model was reduced from approximately 5 h to 5 min. Our results indicate that deep learning has the ability to process US data accurately, representing an important step towards the reconstruction of the fetal heart digital model, which is critical for advancing clinical diagnosis and treatment of CHD during pregnancy.  

Graphical abstract
Keywords
3D printing technology
Congenital heart disease
Deep learning
Reconstruction of ultrasound imaging data
Funding
This work was supported by the Key Research and Development Project of Shaanxi Province (2021LLRH-08).
Conflict of interest
The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing
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