DeepGA for automatically estimating fetal gestational age through ultrasound imaging
Abstract
Accurate estimation of gestational age (GA) is vital for identifying fetal abnormalities. Conventionally, GA is estimated by measuring the morphology of the cranium, abdomen, and femur manually and inputting them into the classic Hadlock formula to assess fetal growth. However, this procedure incurs considerable overhead and suffers from bias caused by the operators, yielding suboptimal estimations. To address this challenge, we develop an automatic DeepGA model to achieve fully automatic GA prediction in an end-to-end manner. Our model uses a deep segmentation model (DeepSeg) to accurately identify and segment three critical tissues, including the cranium, abdomen, and femur, in which their morphology is automatically extracted. After that, we are able to directly estimate the GA via a deep regression model (DeepReg). We evaluate DeepGA on a large dataset, including 10,413 ultrasound images from 7113 subjects. It achieves superior performance over the traditional measurement approach, with a mean absolute estimation error (MAE) of 5 days. Our DeepGA model is a novel automatic solution on the basis of artificial intelligence learning that can help radiologists improve the performance of GA estimation in various clinical scenarios, thereby enhancing the efficiency of prenatal examinations.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Dan et al. developed a DeepGA model that used the three main factors of fetal head, abdomen, and femur.
“Dan et al. developed a DeepGA model that used the three main factors of fetal head, abdomen, and femur [149]”
A benefit of GA AI models was the possibility for application in low-resource countries.
“The benefits of the GA AI models were the possibility for application in low-resource countries [144,145,148,149,152]”
GA AI models promised high accuracy with an error of 3.9 to 5 days in GA estimation.
“promising high accuracy with an error of 3.9 to 5 days in GA estimation [149,152].”
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,474 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01228-7 · Cited by 6,909 · see citing works
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
(2019) · doi:10.1109/cvpr41558.2019 · Cited by 1,796 · see citing works
PointRend: Image Segmentation As Rendering
Kirillov, A., Wu, Y., He, K., & Girshick, R. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00982 · Cited by 1,244 · see citing works
Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan
Salomon, L. J., Alfirevic, Z., Berghella, V., Bilardo, C., Hernandez‐Andrade, E., Johnsen, S. L., Kalache, K., Leung, K. ‐., Malinger, G., Munoz, H., et al. (2011) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.8831 · Cited by 1,006 · see citing works
Antenatal care : routine care for the healthy pregnant woman
Excellence, C. (2008) · RCOG Press eBooks · Cited by 628 · see citing works
Paladini, D., Malinger, G., Monteagudo, A., Timor Tritsch, I., Toi, A., & Pilu, G. (2007) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.3909 · Cited by 577 · see citing works
RETIRED: Determination of Gestational Age by Ultrasound
Butt, K., Lim, K. I., Lim, K., Bly, S., Butt, K., Cargill, Y., Davies, G., Denis, N., Hazlitt, G., Morin, L. M., et al. (2014) · Journal of Obstetrics and Gynaecology Canada · doi:10.1016/s1701-2163(15)30664-2 · Cited by 245 · see citing works
FUIQA: Fetal Ultrasound Image Quality Assessment with Deep Convolutional Networks
Wu, L., Cheng, J.-Z., Li, S., Lei, B., Wang, T., & Ni, D. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2671898 · Cited by 242 · see citing works
Fetal Size and Dating: Charts Recommended for Clinical Obstetric Practice
Loughna, P., Chitty, L. S., Evans, T., & Chudleigh, T. (2009) · Ultrasound · doi:10.1179/174313409x448543 · Cited by 209 · see citing works
New charts for ultrasound dating of pregnancy
Altman, D. G., & Chitty, L. S. (1997) · Ultrasound in Obstetrics and Gynecology · doi:10.1046/j.1469-0705.1997.10030174.x · Cited by 182 · see citing works
Chen, H., Dou, Q., Ni, D., Cheng, J.-Z., Qin, J., Li, S., & Heng, P.-A. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24553-9_62 · Cited by 180 · see citing works
Multi-task learning for quality assessment of fetal head ultrasound images
Lin, Z., Li, S., Ni, D., Liao, Y., Wen, H., Du, J., Chen, S., Wang, T., & Lei, B. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101548 · Cited by 129 · see citing works
Dating and growth in the first trimester
Bottomley, C., & Bourne, T. (2009) · Best Practice & Research Clinical Obstetrics & Gynaecology · doi:10.1016/j.bpobgyn.2009.01.011 · Cited by 98 · see citing works
Ultrasound instead of last menstrual period as the basis of gestational age assignment
Geirsson, R. T. (1991) · Ultrasound in Obstetrics and Gynecology · doi:10.1046/j.1469-0705.1991.01030212.x · Cited by 97 · see citing works
Learning-based prediction of gestational age from ultrasound images of the fetal brain
Namburete, A. I. L., Stebbing, R. V., Kemp, B., Yaqub, M., Papageorghiou, A. T., & Alison Noble, J. (2015) · Medical Image Analysis · doi:10.1016/j.media.2014.12.006 · Cited by 97 · see citing works
Hadlock, F. P., Harrist, R. B., Shah, Y. P., King, D. E., Park, S. K., & Sharman, R. S. (1987) · American Journal of Obstetrics and Gynecology · doi:10.1016/0002-9378(87)90365-6 · Cited by 95 · see citing works
van den Heuvel, T. L. A., Petros, H., Santini, S., de Korte, C. L., & van Ginneken, B. (2019) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2018.09.015 · Cited by 91 · see citing works
Standards (1998) · PubMed · Cited by 77 · see citing works
Hadlock, F. P., Deter, R. L., Harrist, R., & Park, S. K. (1983) · Journal of Clinical Ultrasound · doi:10.1002/jcu.1870110605 · Cited by 76 · see citing works
Kim, B., Kim, K. C., Park, Y., Kwon, J.-Y., Jang, J., & Seo, J. K. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aae255 · Cited by 62 · see citing works
Automatic evaluation of fetal head biometry from ultrasound images using machine learning
Kim, H. P., Lee, S. M., Kwon, J.-Y., Park, Y., Kim, K. C., & Seo, J. K. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab21ac · Cited by 61 · see citing works
Does the Use of Automated Fetal Biometry Improve Clinical Work Flow Efficiency?
Espinoza, J., Good, S., Russell, E., & Lee, W. (2013) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2013.32.5.847 · Cited by 53 · see citing works
Automatic Measurements of Fetal Lateral Ventricles in 2D Ultrasound Images Using Deep Learning
Chen, X., He, M., Dan, T., Wang, N., Lin, M., Zhang, L., Xian, J., Cai, H., & Xie, H. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00526 · Cited by 36 · see citing works
Self-citationYang, J., Dong, X., Hu, Y., Peng, Q., Tao, G., Ou, Y., Cai, H., & Yang, X. (2020) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00385-5 · Cited by 35 · see citing works
Self-citationFetal facial standard plane recognition via very deep convolutional networks
Yu, Z., Ni, D., Chen, S., Li, S., Wang, T., & Lei, B. (2016) · 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590780 · Cited by 22 · see citing works
Automatic detection and measurement of femur length from fetal ultrasonography
Mukherjee, P., Swamy, G., Gupta, M., Patil, U., & Krishnan, K. B. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.844340 · Cited by 17 · see citing works
Coarse-to-fine Nasopharyngeal Carcinoma Segmentation in MRI via Multi-stage Rendering
Yang, L., Peng, H., Dan, T., Hu, Y., Tao, G., & Cai, H. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313574 · Cited by 16 · see citing works
Self-citationLiu, M., Yuan, X., Zhang, Y., Chang, K., Deng, Z., & Xue, J. (2020) · International Symposium on Artificial Intelligence for Medicine Sciences (ISAIMS) · doi:10.1145/3429889.3429903 · Cited by 7 · see citing works
Wang, H., Han, G., Li, H., Tao, G., Zhuo, E., Liu, L., Cai, H., & Ou, Y. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/7562140 · Cited by 6 · see citing works
Self-citationDonald School Textbook of Ultrasound in Obstetrics & Gynecology
Khanem, N. (2007) · The Obstetrician & Gynaecologist · doi:10.1576/toag.9.2.140.27325 · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 39 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Shahrivar, R. Y., Karami, F., & Karami, E. (2023) · Biomimetics · doi:10.3390/biomimetics8070519 · Cited by 57 · see citing works
Review on deep learning fetal brain segmentation from Magnetic Resonance images
Ciceri, T., Squarcina, L., Giubergia, A., Bertoldo, A., Brambilla, P., & Peruzzo, D. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102608 · Cited by 46 · see citing works
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
“The benefits of the GA AI models were the possibility for application in low-resource countries [144,145,148,149,152], even without internet connectivity [145], and in portable devices [148], promising high accuracy with an error of 3.9 to 5 days in GA estimation [149,152].”
BackgroundMethodInfluentialGhabri, H., Alqahtani, M. S., Othman, S. B., Al‐Rasheed, A., Abbas, M. A. E., Almubarak, H. A., Sakli, H., & Abdelkarim, M. N. (2023) · Scientific Reports · doi:10.1038/s41598-023-44689-0 · Cited by 43 · see citing works
Gao, Z., Tian, Z., Pu, B., Li, S., & Li, K. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107399 · Cited by 17 · see citing works
Tadepalli, K., Das, A., Meena, T., & Roy, S. S. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108682 · Cited by 15 · see citing works
Liu, L., She, L., Zheng, Z., Huang, S., & Wu, H. (2025) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.70033 · Cited by 11 · see citing works
Han, X., Yu, J., Yang, X., Chen, C., Zhou, H., Qiu, C., Cao, Y., Zhang, T., Peng, M., Zhu, G., et al. (2024) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-024-06336-y · Cited by 12 · see citing works
Use of artificial intelligence for gestational age estimation: a systematic review and meta-analysis
Naz, S., Noorani, S., Zaidi, S. A. J., Rahman, A. R., Sattar, S., Das, J. K., & Hoodbhoy, Z. A. (2025) · Frontiers in Global Women s Health · doi:10.3389/fgwh.2025.1447579 · Cited by 11 · see citing works
Sivasubramanian, A., Sasidharan, D., Sowmya, V., & Ravi, V. (2025) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-025-01566-6 · Cited by 9 · see citing works
Annamalai, R., & Sindhu, K. G. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-01806-0 · Cited by 4 · see citing works
Judi, A. A., Suresh, P., & Raj, T. A. B. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108706 · Cited by 2 · see citing works
Priyadharshni, S., & Ravi, V. K. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-26835-y · Cited by 2 · see citing works
Huang, S., Wu, H., She, L., & Liu, L. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1712025 · Cited by 2 · see citing works
Fetal Ultrasound Brain Biometry: An Integrated Deep Learning Framework
Kowsalya, B. U., Sankaran, S. M., Mohanram, N., & Thittai, A. K. (2024) · IEEE South Asian Ultrasonics Symposium (SAUS) · doi:10.1109/saus61785.2024.10563575 · Cited by 2 · see citing works
Enhanced Multi-Class Fetal Plane Detection with Limb Localization in Ultrasound Images
Sindhu, K. G., & Annamalai, R. (2024) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc460750.2024.10649084 · Cited by 2 · see citing works
Mishra, D., Chandramohan, V., Sharma, N., Gosain, M., Wadhwa, N., Natchu, U. C. M., GARBH-Ini study group, Khurana, A., Noble, J. A., Thiruvengadam, R., et al. (2024) · medRxiv · doi:10.1101/2024.05.13.24305466 · Cited by 2 · see citing works
AGE-US: Automated Gestational Age Estimation Based on Fetal Ultrasound Images
Parga, C. D., Nuñez‐Garcia, M., Carreira, M. J., Bernardino, G., & Vila-Blanco, N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-99568-2_7 · Cited by 1 · see citing works
Automated interpretation of fetal cardiac function evaluation from the echocardiogram
Huang, C., Zhang, L., Xie, B., Jiang, Y., Zhu, Y., Liu, X., Lei, T., He, M., Yan, Y., Wang, N., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02381-3 · Cited by 1 · see citing works
Self-citationEstimating Fetal Gestational Age from Ultrasound Images Using Deep Learning Techniques
Suganthi, N. M., Subhashni, R., Chitra, A., Rajakumari, S., Priya, T., & Anitha, P. (2025) · International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI) · doi:10.1109/icmsci62561.2025.10894429
Longitud de la escápula fetal como predictora de la edad gestacional
Reyna‐Villasmil, E., Rondón-Tapía, M., Torres-Cepeda, D., Mejía-Montilla, J., Reyna-Villasmil, N., Fernández-Ramírez, A., & Briceño-Pérez, C. (2025) · Revista Repertorio de Medicina y Cirugía · doi:10.31260/repertmedcir.01217372.1517
Emerging applications of artificial intelligence for obstetric ultrasound: A scoping review
Gupta, V., Santos, N., Ripa, S. D., & Walker, D. A. (2026) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.70789 · Cited by 2 · see citing works
Luo, S., Liu, M., Lv, N., Dai, G., Ma, K., Zhan, M. J., Sun, Y.-X., Yang, H.-K., Deng, Z.-H., Wang, Y., et al. (2026) · Pediatric Radiology · doi:10.1007/s00247-026-06536-y
UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image
Zhou, Z., Chen, Q., Cao, X., Chen, C., Sun, C., & Du, B. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114787
Cârlig, O., Iovoaica-Rămescu, C., Jurma, F. C., & Iliescu, D. G. (2026) · Diagnostics · doi:10.3390/diagnostics16183063
- Authors
- Dan, T., Chen, X., He, M., Guo, H., He, X., Chen, J., Xian, J., Hu, Y., Zhang, B., Wang, N., Xie, H., & Cai, H.
- Affiliations
- South China University of Technology (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Xiamen University (CN)
- Corresponding author
- Xie, Hongning
- Cai, Hongmin
- ORCID
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 135
- Pages
- 102453
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W4309635276
- Cited by
- 25
- References
- 39
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 5 · 2023: 5
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Update notices
- erratum (2024-03-01)
- Open access
- closed
- Licence
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Keywords
- prenatal ultrasound, femur segmentation, Hadlock formula, prenatal examination
- MeSH terms
- Artificial Intelligence, Female, Gestational Age, Head, Humans, Pregnancy, Ultrasonography, Ultrasonography, Prenatal
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
