[an error occurred while processing this directive]

Advanced Ultrasound in Diagnosis and Therapy ›› 2025, Vol. 9 ›› Issue (4): 449-456.doi: 10.26599/AUDT.2025.250095

• • 上一篇    下一篇

  

  • 收稿日期:2025-09-07 修回日期:2025-09-14 接受日期:2025-10-02 出版日期:2025-12-30 发布日期:2025-11-06

Current Applications of Artificial Intelligence in Obstetric Ultrasound

Li Yanrana, Cui Yuanjiea, Wu Qingqinga,*(), Zhang Naa,*()   

  1. aDepartment of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China
  • Received:2025-09-07 Revised:2025-09-14 Accepted:2025-10-02 Online:2025-12-30 Published:2025-11-06
  • Contact: Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, No.251 Yaojiayuan Road, Chaoyang District, Beijing, 100026, China (Qingqing Wua, Na Zhang)e-mail: qingqingwu@ccmu.edu.cn (QQ W);nana8022@ccmu.edu.cn (N Z)

RichHTML

4

PDF (PC)

348

PDF (Mobile)

1

Abstract:

Artificial Intelligence (AI) technology has made remarkable progress in fetal ultrasound examinations, particularly excelling in fetal growth monitoring, organ function assessment, and early disease diagnosis. By automating the analysis of fetal ultrasound images, AI can accurately measure fetal biometric parameters and assist in diagnosing issues such as fetal growth restriction and organ developmental abnormalities. It demonstrates significant application potential in evaluating multiple organ systems including the fetal lungs, nervous system, cardiovascular system, and placenta, substantially enhancing the efficiency and accuracy of prenatal screening. This paper aims to review the current status of AI applications in obstetric ultrasound, while also exploring its limitations and future prospects.

Key words: Artificial intelligence, Obstetric ultrasound, Deep learning, Convolutional neural networks, Machine learning

"

"

[1] Xun SY , Ke W , Jiang MF , Chen HC , Chen HM , Lam CT , et al . Current status, prospect and bottleneck of ultrasound AI development: a systemic review. Advanced Ultrasound in Diagnosis and Therapy 2023; 7: 61-72.
doi: 10.37015/AUDT.2023.230020
[2] Ghelich Oghli M , Shabanzadeh A , Moradi S , Sirjani N , Gerami R , Ghaderi P , et al . Automatic fetal biometry prediction using a novel deep convolutional network architecture. Phys Med 2021; 88: 127-137.
doi: 10.1016/j.ejmp.2021.06.020
[3] Ashkani Chenarlogh V , Ghelich Oghli M , Shabanzadeh A , Sirjani N , Akhavan A , Shiri I , et al . Fast and accurate u-net model for fetal ultrasound image segmentation. Ultrason Imaging 2022; 44: 25-38.
doi: 10.1177/01617346211069882
[4] Dubey G , Srivastava S , Jayswal AK , Saraswat M , Singh P , Memoria M . Fetal ultrasound segmentation and measurements using appearance and shape prior based density regression with deep CNN and robust ellipse fitting. J Imaging Inform Med 2024; 37: 247-267.
doi: 10.1007/s10278-023-00908-8
[5] Burgos-Artizzu XP , Coronado-Gutiérrez D , Valenzuela-Alcaraz B , Vellvé K , Eixarch E , Crispi F , et al . Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age. Am J Obstet Gynecol MFM 2021; 3: 100462.
doi: 10.1016/j.ajogmf.2021.100462
[6] Guo WJ , Li SL , Yu X , Wen HX , Yuan Y , Yang X . Artificial intelligence in prenatal ultrasound: clinical application and prospect. Advanced Ultrasound in Diagnosis and Therapy 2023; 7: 82-90.
doi: 10.37015/AUDT.2023.230024
[7] Płotka S , Klasa A , Lisowska A , Seliga-Siwecka J , Lipa M , Trzciński T , et al . Deep learning fetal ultrasound video model match human observers in biometric measurements. Phys Med Biol 2022; 67: 10
[8] Lee LH , Bradburn E , Craik R , Yaqub M , Norris SA , Ismail LC , et al . Machine learning for accurate estimation of fetal gestational age based on ultrasound images. NPJ Digit Med 2023; 6: 36.
doi: 10.1038/s41746-023-00774-2
[9] Patel N , O'Brien J , Bunn R , Schanbacher B , Bauer J , Lam GK . Perinatal artificial intelligence in ultrasound (PAIR) study: predicting delivery timing. J Matern Fetal Neonatal Med 2025; 38: 2532099.
doi: 10.1080/14767058.2025.2532099
[10] Xia TH , Tan M , Li JH , Wang JJ , Wu QQ , Kong DX . Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation. Chin Med J (Engl) 2021; 134: 1828-1837.
doi: 10.1097/CM9.0000000000001547
[11] Palacio M , Bonet-Carne E , Cobo T , Perez-Moreno A , Sabrià J , Richter J , et al . Prediction of neonatal respiratory morbidity by quantitative ultrasound lung texture analysis: a multicenter study. Am J Obstet Gynecol 2017; 217: 196.e1-14.
doi: 10.1016/j.ajog.2017.03.016
[12] Zamarian ACP , Caetano ACR , Grohmann RM , Mazzola JB , Milani HJF , Passos JP , et al . Prediction of lung maturity in fetuses with growth restriction through quantitative ultrasound analysis. Ultrasound Med Biol 2022; 48: 20-26.
doi: 10.1016/j.ultrasmedbio.2021.08.020
[13] Rabachini Caetano AC , Machado Nardozza LM , Perez Zamarian AC , Silva Drumond LG , Chiaratti de Oliveira A , Dualib PM , et al . Prediction of lung maturity through quantitative ultrasound analysis of fetal lung texture in women with diabetes during pregnancy. J Perinat Med 2023; 51: 913-919.
doi: 10.1515/jpm-2023-0009
[14] Du Y , Fang Z , Jiao J , Xi G , Zhu C , Ren Y , et al . Application of ultrasound-based radiomics technology in fetal-lung-texture analysis in pregnancies complicated by gestational diabetes and/or pre-eclampsia. Ultrasound Obstet Gynecol 2021; 57: 804-812.
doi: 10.1002/uog.22037
[15] Moreno-Espinosa AL , Hawkins-Villarreal A , Burgos-Artizzu XP , Coronado-Gutierrez D , Castelazo S , Lip-Sosa DL , et al . Concordance of the risk of neonatal respiratory morbidity assessed by quantitative ultrasound lung texture analysis in fetuses of twin pregnancies. Sci Rep 2022; 12: 9016.
doi: 10.1038/s41598-022-13047-x
[16] Flosi FB , da Silva FC , de Jesús GR , Velarde LGC , de Sá RAM . Assessment of fetal lung maturity using quantitative ultrasound analysis in patients with prelabor rupture of membranes. Fetal Diagn Ther 2020; 47: 636-641.
doi: 10.1159/000507550
[17] Hesse LS , Aliasi M , Moser F , INTERGROWTH-21(st) Consortium; Haak MC , Xie W , et al . Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning. Neuroimage 2022; 254: 119117.
doi: 10.1016/j.neuroimage.2022.119117
[18] Chen X , He M , Dan T , Wang N , Lin M , Zhang L , et al . Automatic measurements of fetal lateral ventricles in 2d ultrasound images using deep learning. Front Neurol 2020; 11: 526.
doi: 10.3389/fneur.2020.00526
[19] Xie HN , Wang N , He M , Zhang LH , Cai HM , Xian JB , et al . Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal. Ultrasound Obstet Gynecol 2020; 56: 579-587.
doi: 10.1002/uog.21967
[20] Xie B , Lei T , Wang N , Cai H , Xian J , He M , et al . Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks. Int J Comput Assist Radiol Surg 2020; 15: 1303-1312.
doi: 10.1007/s11548-020-02182-3
[21] Lin M , He X , Guo H , He M , Zhang L , Xian J , et al . Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations. Ultrasound Obstet Gynecol 2022; 59: 304-316.
doi: 10.1002/uog.24843
[22] Herling L , Johnson J , Ferm-Widlund K , Zamprakou A , Westgren M , Acharya G . Automated quantitative evaluation of fetal atrioventricular annular plane systolic excursion. Ultrasound Obstet Gynecol 2021; 58: 853-863.
doi: 10.1002/uog.23703
[23] Qiao S , Pan S , Luo G , Pang S , Chen T , Singh AK , et al . A pseudo-siamese feature fusion generative adversarial network for synthesizing high-quality fetal four-chamber views. IEEE J Biomed Health Inform 2023; 27: 1193-1204.
doi: 10.1109/JBHI.2022.3143319
[24] Ma M , Li Y , Chen R , Huang C , Mao Y , Zhao B . Diagnostic performance of fetal intelligent navigation echocardiography (FINE) in fetuses with double-outlet right ventricle (DORV). Int J Cardiovasc Imaging 2020; 36: 2165-2172.
doi: 10.1007/s10554-020-01932-3
[25] Huang C , Zhao BW , Chen R , Pang HS , Pan M , Peng XH , et al. Is Fetal intelligent navigation echocardiography helpful in screening for d-transposition of the great arteries? J Ultrasound Med 2020; 39:775-784.
[26] Sakai A , Komatsu M , Komatsu R , Matsuoka R , Yasutomi S , Dozen A , et al . Medical professional enhancement using explainable artificial intelligence in fetal cardiac ultrasound screening. Biomedicines 2022; 10: 551.
doi: 10.3390/biomedicines10030551
[27] Arnaout R , Curran L , Zhao Y , Levine JC , Chinn E , Moon-Grady AJ . An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med 2021; 27: 882-891.
doi: 10.1038/s41591-021-01342-5
[28] Ungureanu A , Marcu AS , Patru CL , Ruican D , Nagy R , Stoean R , et al . Learning deep architectures for the interpretation of first-trimester fetal echocardiography (LIFE) - a study protocol for developing an automated intelligent decision support system for early fetal echocardiography. BMC Pregnancy Childbirth 2023; 23: 20.
doi: 10.1186/s12884-022-05204-x
[29] Torrents-Barrena J , Monill N , Piella G , Gratacós E , Eixarch E , Ceresa M , et al . Assessment of radiomics and deep learning for the segmentation of fetal and maternal anatomy in magnetic resonance imaging and ultrasound. Acad Radiol 2021; 28: 173-188.
doi: 10.1016/j.acra.2019.11.006
[30] Schilpzand M , Neff C , van Dillen J , van Ginneken B , Heskes T , de Korte C , et al . Automatic placenta localization from ultrasound imaging in a resource-limited setting using a predefined ultrasound acquisition protocol and deep learning. Ultrasound Med Biol 2022; 48: 663-674.
doi: 10.1016/j.ultrasmedbio.2021.12.006
[31] Gupta K , Balyan K , Lamba B , Puri M , Sengupta D , Kumar M . Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy. J Matern Fetal Neonatal Med 2022; 35: 5587-5594.
doi: 10.1080/14767058.2021.1887847
[32] Miyagi Y , Hata T , Bouno S , Koyanagi A , Miyake T . Recognition of facial expression of fetuses by artificial intelligence (AI). J Perinat Med 2021; 49: 596-603.
doi: 10.1515/jpm-2020-0537
[33] Smail LC , Dhindsa K , Braga LH , Becker S , Sonnadara RR . Using deep learning algorithms to grade hydronephrosis severity: toward a clinical adjunct. Front Pediatr 2020; 8: 1.
doi: 10.3389/fped.2020.00001
[34] Weerasinghe NH , Lovell NH , Welsh AW , Stevenson GN . Multi-parametric fusion of 3D power doppler ultrasound for fetal kidney segmentation using fully convolutional neural networks. IEEE J Biomed Health Inform 2021; 25: 2050-2027.
doi: 10.1109/JBHI.2020.3027318
[35] Shozu K , Komatsu M , Sakai A , Komatsu R , Dozen A , Machino H , et al . Model-agnostic method for thoracic wall segmentation in fetal ultrasound videos. Biomolecules 2020; 10: 1691.
doi: 10.3390/biom10121691
[36] Cho HC , Sun S , Min Hyun C , Kwon JY , Kim B , Park Y , et al . Automated ultrasound assessment of amniotic fluid index using deep learning. Med Image Anal 2021; 69: 101951.
doi: 10.1016/j.media.2020.101951
[37] Calhoun BC , Uselman H , Olle EW . Development of artificial intelligence image classification models for determination of umbilical cord vascular anomalies. J Ultrasound Med 2024; 43: 881-897.
doi: 10.1002/jum.16418
[38] Lei T , Zheng Q , Feng J , Zhang L , Zhou Q , He M , et al . Enhancing trainee performance in obstetric ultrasound through an artificial intelligence system: randomized controlled trial. Ultrasound Obstet Gynecol 2024; 64: 453-462.
doi: 10.1002/uog.29101
[39] Ramirez Zegarra R , Conversano F , Dall'Asta A , Giovanna Di Trani M , Fieni S , Morello R , et al . A deep learning approach to identify the fetal head position using transperineal ultrasound during labor. Eur J Obstet Gynecol Reprod Biol 2024; 301: 147-153.
doi: 10.1016/j.ejogrb.2024.08.012
[40] Chen RD , Wang XQ , Liang P , Ouyang XP , Kong DX . Intelligent ultrasonic diagnosis and clinical application: technical development and prospectives. Advanced Ultrasound in Diagnosis and Therapy 2023; 7: 73-81.
doi: 10.37015/AUDT.2023.230019
[41] Burgos-Artizzu XP , Coronado-Gutiérrez D , Valenzuela-Alcaraz B , Bonet-Carne E , Eixarch E , Crispi F , et al . Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci Rep 2020; 10: 10200.
doi: 10.1038/s41598-020-67076-5
[42] Ramirez Zegarra R , Ghi T . Use of artificial intelligence and deep learning in fetal ultrasound imaging. Ultrasound Obstet Gynecol 2023; 62: 185-194
No related articles found!
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
[an error occurred while processing this directive]