Artificial Intelligence in Prenatal Ultrasound: Clinical Application and Prospect

doi:10.37015/AUDT.2023.230024

Abstract

Abstract:

Since the 1990s, researchers have been seeking approaches for applying artificial intelligence (AI) to prenatal ultrasound. With the breakthrough of cloud computing technology and the development of deep learning technology, AI in prenatal ultrasound has already entered the clinical application stage in recent years. How does AI combine with clinical prenatal ultrasound? Is the clinical application of AI in prenatal ultrasound effective? What can we expect from AI in prenatal ultrasound? This review introduces the latest developments in this field and explores the challenges and opportunities brought by AI to prenatal ultrasound.

Key words: Ultrasound; Artificial intelligence; Prenatal diagnosis

Wenjia Guo, MM, Shengli Li, MM, Xing Yu, MD, Huaxuan Wen, BM, Ying Yuan, MM, Xia Yang, MM. Artificial Intelligence in Prenatal Ultrasound: Clinical Application and Prospect. Advanced Ultrasound in Diagnosis and Therapy, 2023, 7(2): 82-90.

Figures/Tables 5

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

References 38

[1]	Zador IE, Salari V, Chik L, Sokol RJ. Ultrasound measurement of the fetal head: computer versus operator. Ultrasound Obstet Gynecol 1991; 1:208-211. doi: 10.1046/j.1469-0705.1991.01030208.x
[2]	Chalana V, Winter TC, Cyr DR, Haynor DR, Kim Y. Automatic fetal head measurements from sonographic images. Acad Radiol 1996; 3:628-635. pmid: 8796726
[3]	Chalana V, Kim Y. A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging 1997; 16:642-652. doi: 10.1109/42.640755
[4]	Lei B, Tan EL, Chen SP, Zhuo L, Li SL, Ni D, et al. Automatic recognition of fetal facial standard plane in ultrasound image via fisher vector. PLoS One 2015; 10:e0121838.
[5]	Kim B, Kim KC, Park Y, Kwon JY, Jang J, Seo JK. Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images. Physiol Meas 2018; 39:105007. doi: 10.1088/1361-6579/aae255
[6]	Lee YB, Kim MJ, Kim MH. Robust border enhancement and detection for measurement of fetal nuchal translucency in ultrasound images. Med Bio Eng Comput 2007; 45:1143-1152. doi: 10.1007/s11517-007-0225-7
[7]	Foi A, Maggioni M, Pepe A, Rueda S, Noble JA, Papageorghiou AT, et al. Difference of gaussians revolved along elliptical paths for ultrasound fetal head segmentation. Comput Med Imag Grap 2014; 38:774-784. doi: 10.1016/j.compmedimag.2014.09.006 pmid: 25450760
[8]	Wang WM, Qin J, Zhu L, Ni D, Chui YP, Heng PA. Detection and measurement of fetal abdominal contour in ultrasound images via local phase information and iterative randomized hough transform. Bio-Med Mater Eng 2014; 24:1261-1267.
[9]	Zhang L, Chen SP, Chin CT, Wang TF, Li SL. Intelligent scanning: automated standard plane selection and biometric measurement of early gestational sac in routine ultrasound examination. Med Phys 2012; 39:5015-5027. doi: 10.1118/1.4736415 pmid: 22894427
[10]	Chen H, Ni D, Qin J, Li SL, Yang X, Wang TF, et al. Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J Biomed Health Inform 2015; 19:1627-1636. doi: 10.1109/JBHI.2015.2425041
[11]	He FJ, Wang YQ, Xiu Y, Zhang YX, Chen LZ. Artificial intelligence in prenatal ultrasound diagnosis. Front. Med 2021; 8:729978. doi: 10.3389/fmed.2021.729978
[12]	Chen H, Wu LY, Dou Q, Qin J, Li SL, Cheng JZ, et al. Ultrasound standard plane detection using a composite neural network framework. IEEE Trans Cybern 2017; 47:1576-1586. doi: 10.1109/TCYB.2017.2685080
[13]	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.v59.3
[14]	Baumgartner CF, Kamnitsas K, Matthew J, Fletcher TP, Smith S, Koch LM, et al. SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans Med Imaging 2017; 36:2204-2215. doi: 10.1109/TMI.42
[15]	NiD, Yang X, Chen X, Chin CT, Chen SP, Heng PA, et al. Standard plane localization in ultrasound by radial component model and selective search. Ultrasound Med Biol 2014; 40:2728-2742 doi: 10.1016/j.ultrasmedbio.2014.06.006 pmid: 25220278
[16]	Luo DD, Wen HX, Peng GY, Lin Y, Liang ML, Liao YM, et al. A prenatal ultrasound scanning approach: one-touch technique in second and third trimesters. Ultrasound Med Biol 2021; 47:2258-2265. doi: 10.1016/j.ultrasmedbio.2021.04.020 pmid: 34059375
[17]	Guo WJ, Luo DD, Xu H, Liu YX, He HL, Tan HM, et al. Novel intelligent ultrasound Smart Fetus Advanced (SFA) technique for automatic distinguishing and acquiring fetal standard sections. Chinese Journal of Medical Imaging Technology 2023; 39:65-69.
[18]	Matthew J, Skelton E, Day TG, Zimmer VA, Gomez A, Wheeler G, et al. Exploring a new paradigm for the fetal anomaly ultrasound scan: artificial intelligence in real time. Prenatal Diag 2022; 42:49-59. doi: 10.1002/pd.v42.1
[19]	Hur H, Kim YH, Cho HY, Park YW, Won HS, Lee MY, et al. Feasibility of three-dimensional reconstruction and automated measurement of fetal long bones using 5D long bone. Obstet Gynecol Sci 2015; 58:268-276. doi: 10.5468/ogs.2015.58.4.268 pmid: 26217596
[20]	Grandjean GA, Hossu G, Bertholdt C, Noble P, Morel O, Grangé G. Artificial intelligence assistance for fetal head biometry: assessment of automated measurement software. Diagn Interv Imaging 2018; 99:709-716. doi: 10.1016/j.diii.2018.08.001
[21]	Meng L, Zhao D, Yang ZY, Wang B. Automatic display of fetal brain planes and automatic measurements of fetal brain parameters by transabdominal three-dimensional ultrasound. J Clin Ultrasound 2020; 48:82-88. doi: 10.1002/jcu.22762 pmid: 31318058
[22]	Rizzo G, Aiello E, Pietrolucci ME, Arduini D. The feasibility of using 5D CNS software in obtaining standard fetal head measurements from volumes acquired by three-dimensional ultrasonography: comparison with two-dimensional ultrasound. J Matern-Fetal Neo M 2016; 29:2217-2210.
[23]	Welp A, Gembicki M, Dracopoulos C, Scharf JL, Rody A, Weichert J. Applicability of a semiautomated volumetric approach (5D CNS+™) for detailed antenatal reconstruction of abnormal fetal CNS anatomy. BMC medical imaging 2022; 22:154. doi: 10.1186/s12880-022-00888-1 pmid: 36056307
[24]	Hu WY, Zhou JH, Tao XY, Li SY, Wang B, Zhao BW. Novel foetal echocardiographic image processing software (5D Heart) improves the display of key diagnostic elements in foetal echocardiography. BMC medical imaging 2020; 20:33. doi: 10.1186/s12880-020-00429-8 pmid: 32245426
[25]	Yeo L, Romero R. Color and power Doppler combined with fetal intelligent navigation echocardiography (FINE) to evaluate the fetal heart. Ultrasound Obstet Gynecol 2017; 50:476-491. doi: 10.1002/uog.17522 pmid: 28809063
[26]	Yeo L, Luewan S, Romero R. Fetal intelligent navigation echocardiography (FINE) detects 98% of congenital heart disease. J Ultras medicine : official journal of the American Institute of Ultrasound in Med 2018; 37:2577-2593.
[27]	Group of Ultrasound in Obstetrics and Gynecology, Ultrasound Branch of Chinese Medical Association; Medical Imaging Group of the National Prenatal Diagnosis Expert Group, Department of Maternal and Child, National Health Commission of the People's Republic of China. Practice Guidelines for Performance of Prenatal Ultrasound Screening 2022; 31:1-12.
[28]	Salomon LJ, Alfirevic Z, Costa FDS, Deter RL, Figueras F, Ghi T, et al. ISUOG practice guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound Obstet Gynecol 2019; 53:715-723. doi: 10.1002/uog.20272 pmid: 31169958
[29]	Granozio G, Napolitano R. Quality control of fetal biometric evaluation and Doppler ultrasound. Minerva Obstet Gynecol 2021; 73:415-422.
[30]	Wu LY, Cheng JZ, Li SL, Lei BY, Wang YF, Ni D. FUIQA: Fetal Ultrasound Image Quality Assessment with deep convolutional networks. IEEE Trans Cybern 2017; 47:1336-1349. doi: 10.1109/TCYB.2017.2671898
[31]	Lin ZH, Li SL, Ni D, Liao YM, Wen HX, Du J, et al. Multi-task learning for quality assessment of fetal head ultrasound images. Med Image Anal 2019; 58:101548 doi: 10.1016/j.media.2019.101548
[32]	Zhang B, Liu H, Luo H, Li KJ. Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learning. Medicine 2021; 100:e24427. doi: 10.1097/MD.0000000000024427
[33]	Tan Y, Wen HX, Peng GY, Luo DD, Wen X, Jiang Y, et al. Effectiveness of obstetric intelligent ultrasonic quality control system. Chinese Journal of Medical Imaging Technology 2022; 38:1361-1366.
[34]	Peng GY, Tan Y, Zeng Q, Luo DD, Huang WL, Jiang Y, et al. Value of artificial Intelligence quality control in improving the standard rate of transverse section of fetal abdomen. Chinese Journal of Prenatal Diagnosis (Electronic Version) 2022; 04:6-10+31.
[35]	Droste R, Drukker L, Papageorghiou AT, Noble JA. Automatic probe movement guidance for freehand obstetric ultrasound. Med Image Comput Comput Assist Interv 2020; 12263:583-592. doi: 10.1007/978-3-030-59716-0_56 pmid: 33103163
[36]	Zhao C, Droste R, Drukker L, Papageorghiou AT, Noble JA. Visual-assisted probe movement guidance for obstetric ultrasound scanning using landmark retrieval. Med Image Comput Comput Assist Interv 2021; 12908:670-679 doi: 10.1007/978-3-030-87237-3_64 pmid: 35373220
[37]	Gomes RG, Vwalika B, Lee C, Willis A, Sieniek M, Price JT, et al. A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment. Commun Med 2022; 2:128. doi: 10.1038/s43856-022-00194-5 pmid: 36249461
[38]	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 pmid: 33548168