Artificial intelligence in obstetric ultrasound: A scoping review.
Journal
Prenatal diagnosis
ISSN: 1097-0223
Titre abrégé: Prenat Diagn
Pays: England
ID NLM: 8106540
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
revised:
05
06
2023
received:
27
03
2023
accepted:
17
07
2023
medline:
15
8
2023
pubmed:
28
7
2023
entrez:
28
7
2023
Statut:
ppublish
Résumé
The objective is to summarize the current use of artificial intelligence (AI) in obstetric ultrasound. PubMed, Cochrane Library, and ClinicalTrials.gov databases were searched using the following keywords "neural networks", OR "artificial intelligence", OR "machine learning", OR "deep learning", AND "obstetrics", OR "obstetrical", OR "fetus", OR "foetus", OR "fetal", OR "foetal", OR "pregnancy", or "pregnant", AND "ultrasound" from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors' search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1176-1219Informations de copyright
© 2023 The Authors. Prenatal Diagnosis published by John Wiley & Sons Ltd.
Références
Artificial intelligence technologies. United Kingdom Engineering and Physical Sciences Research Council. Accessed December 12, 2022. https://epsrc.ukri.org/research/ourportfolio/researchareas/ait/
He F, Wang Y, Xiu Y, sinclair Y, Chen L. Artificial intelligence in prenatal ultrasound diagnosis. Front Med. 2021;8:729978. https://doi.org/10.3389/fmed.2021.729978
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127-157. https://doi.org/10.3322/caac.21552
Choi YJ, Baek JH, Park HS, et al. A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of thyroid nodules on ultrasound: initial clinical assessment. Thyroid. 2017;27(4):546-552. https://doi.org/10.1089/thy.2016.0372
Nishida N, Kudo M. Artificial intelligence in medical imaging and its application in sonography for the management of liver tumor. Front Oncol. 2020;10:594580. https://doi.org/10.3389/fonc.2020.594580
Liang X, Yu J, Liao J, Chen Z. Convolutional neural network for breast and thyroid nodules diagnosis in ultrasound imaging. BioMed Res Int. 2020;2020:1763803-1763809. https://doi.org/10.1155/2020/1763803
Lei YM, Yin M, Yu MH, et al. Artificial intelligence in medical imaging of the breast. Front Oncol. 2021;11:600557. https://doi.org/10.3389/fonc.2021.600557
Zhang L, Ye X, Lambrou T, Duan W, Allinson N, Dudley NJ. A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2D ultrasound images. Phys Med Biol. 2016;61(3):1095-1115. https://doi.org/10.1088/0031-9155/61/3/1095
Li P, Zhao H, Liu P, Cao F. Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images. Med Biol Eng Comput. 2020;58(11):2879-2892. https://doi.org/10.1007/s11517-020-02242-5
van den Heuvel TLA, Petros H, Santini S, de Korte CL, van Ginneken B. Automated fetal head detection and circumference estimation from free-hand ultrasound sweeps using deep learning in resource-limited countries. Ultrasound Med Biol. 2019;45(3):773-785. https://doi.org/10.1016/j.ultrasmedbio.2018.09.015
Sinclair M, Baumgartner CF, Matthew J, et al. Human-Level Performance on Automatic Head Biometrics in Fetal Ultrasound Using Fully Convolutional Neural Networks. IEEE; 2018:714-717.
Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467-473. https://doi.org/10.7326/m18-0850
Sciortino G, Tegolo D, Valenti C. Automatic detection and measurement of nuchal translucency. Comput Biol Med. 2017;82:12-20. https://doi.org/10.1016/j.compbiomed.2017.01.008
Park J, Sofka M, Lee S, Kim D, Zhou SK. Automatic nuchal translucency measurement from ultrasonography. Med Image Comput Comput Assist Interv. 2013;16(Pt 3):243-250. https://doi.org/10.1007/978-3-642-40760-4_31
Deng Y, Wang Y, Chen P, Yu J. A hierarchical model for automatic nuchal translucency detection from ultrasound images. Comput Biol Med. 2012;42(6):706-713. https://doi.org/10.1016/j.compbiomed.2012.04.002
Borenstein M, Azumendi Perez G, Molina Garcia F, Romero M, Anderica JR. Gestational sac volume: comparison between SonoAVC and VOCAL measurements at 11 + 0 to 13 + 6 weeks of gestation. Ultrasound Obstet Gynecol. 2009;34(5):510-514. https://doi.org/10.1002/uog.7342
Zhang L, Chen S, Chin CT, Wang T, Li S. Intelligent scanning: automated standard plane selection and biometric measurement of early gestational sac in routine ultrasound examination. Med Phys. 2012;39(8):5015-5027. https://doi.org/10.1118/1.4736415
Zhang L, Chen S, Li S, Wang T. Automatic measurement of early gestational sac diameters from one scan session. 2011.
Sciortino G, Orlandi E, Valenti C, Tegolo D. Wavelet analysis and neural network classifiers to detect mid-sagittal sections for nuchal translucency measurement. Image Anal Stereol. 2016;35(2):105. https://doi.org/10.5566/ias.1352
Tsai PY, Hung CH, Chen CY, Sun YN. Automatic fetal middle sagittal plane detection in ultrasound using generative adversarial network. Diagnostics. 2020;11(1):21. https://doi.org/10.3390/diagnostics11010021
Nie S, Yu J, Chen P, Wang Y, Zhang JQ. Automatic detection of standard sagittal plane in the first trimester of pregnancy using 3-D ultrasound data. Ultrasound Med Biol. 2017;43(1):286-300. https://doi.org/10.1016/j.ultrasmedbio.2016.08.034
Ryou H, Yaqub M, Cavallaro A, Papageorghiou AT, Alison Noble J. Automated 3D ultrasound image analysis for first trimester assessment of fetal health. Phys Med Biol. 2019;64(18):185010. https://doi.org/10.1088/1361-6560/ab3ad1
Gofer S, Haik O, Bardin R, Gilboa Y, Perlman S. Machine learning algorithms for classification of first-trimester fetal brain ultrasound images. J Ultrasound Med. 2022;41(7):1773-1779. https://doi.org/10.1002/jum.15860
Looney P, Stevenson GN, Nicolaides KH, et al. Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning. JCI Insight. 2018;3(11). https://doi.org/10.1172/jci.insight.120178
Torrents-Barrena J, Monill N, Piella G, 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(2):173-188. https://doi.org/10.1016/j.acra.2019.11.006
Hu R, Singla R, Yan R, Mayer C, Rohling RN. Automated placenta segmentation with a convolutional neural network weighted by acoustic shadow detection. 2019:6718-6723.
Schilpzand M, Neff C, van Dillen J, 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(4):663-674. https://doi.org/10.1016/j.ultrasmedbio.2021.12.006
Saavedra AC, Arroyo J, Tamayo L, Egoavil M, Ramos B, Castaneda B. Automatic ultrasound assessment of placenta previa during the third trimester for rural areas. 2020:1-4.
Lei B, Yao Y, Chen S, et al. Discriminative learning for automatic staging of placental maturity via multi-layer Fisher vector. Sci Rep. 2015;5(1):12818. https://doi.org/10.1038/srep12818
Qi H, Collins S, Noble JA. Automatic lacunae localization in placental ultrasound images via layer aggregation. Med Image Comput Comput Assist Interv. 2018;11071:921-929. https://doi.org/10.1007/978-3-030-00934-2_102
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(25):5587-5594. https://doi.org/10.1080/14767058.2021.1887847
Plotka S, Klasa A, Lisowska A, et al. Deep learning fetal ultrasound video model match human observers in biometric measurements. Phys Med Biol. 2022;16(4):67. https://doi.org/10.1088/1361-6560/ac4d85
Arroyo J, Marini TJ, Saavedra AC, et al. No sonographer, no radiologist: new system for automatic prenatal detection of fetal biometry, fetal presentation, and placental location. PLoS One. 2022;17(2):e0262107. https://doi.org/10.1371/journal.pone.0262107
Prieto JC, Shah H, Rosenbaum AJ, et al. An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation. Proc SPIE-Int Soc Opt Eng. 2021;11596. https://doi.org/10.1117/12.2582243
Ghelich Oghli M, Shabanzadeh A, Moradi S, et al. Automatic fetal biometry prediction using a novel deep convolutional network architecture. Phys Med. 2021;88:127-137. https://doi.org/10.1016/j.ejmp.2021.06.020
Carneiro G, Georgescu B, Good S, Comaniciu D. Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree. IEEE Trans Med Imag. 2008;27(9):1342-1355. https://doi.org/10.1109/TMI.2008.928917
Carneiro G, Georgescu B, Good S, Comaniciu D. Automatic fetal measurements in ultrasound using constrained probabilistic boosting tree. Med Image Comput Comput Assist Interv. 2007;10(Pt 2):571-579. https://doi.org/10.1007/978-3-540-75759-7_69
Rueda S, Fathima S, Knight CL, et al. Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans Med Imag. 2014;33(4):797-813. https://doi.org/10.1109/TMI.2013.2276943
Bano S, Dromey B, Vasconcelos F, et al. AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes. Springer International Publishing; 2021:228-238.
Luo D, Wen H, Peng G, et al. A prenatal ultrasound scanning approach: one-touch technique in second and third trimesters. Ultrasound Med Biol. 2021;47(8):2258-2265. https://doi.org/10.1016/j.ultrasmedbio.2021.04.020
Rong Y, Xiang D, Zhu W, et al. Deriving external forces via convolutional neural networks for biomedical image segmentation. Biomed Opt Express. 2019;10(8):3800-3814. https://doi.org/10.1364/BOE.10.003800
Ambroise Grandjean G, Hossu G, Bertholdt C, Noble P, Morel O, Grange G. Artificial intelligence assistance for fetal head biometry: assessment of automated measurement software. Diagn Interv Imaging. 2018;99(11):709-716. https://doi.org/10.1016/j.diii.2018.08.001
Pluym ID, Afshar Y, Holliman K, et al. Accuracy of automated three-dimensional ultrasound imaging technique for fetal head biometry. Ultrasound Obstet Gynecol. 2021;57(5):798-803. https://doi.org/10.1002/uog.22171
Kim HP, Lee SM, Kwon JY, Park Y, Kim KC, Seo JK. Automatic evaluation of fetal head biometry from ultrasound images using machine learning. Physiol Meas. 2019;40(6):065009. https://doi.org/10.1088/1361-6579/ab21ac
Perez-Gonzalez JL, Muńoz JCB, Porras MCR, Arámbula-Cosío F, Medina-Bańuelos V. Automatic fetal head measurements from ultrasound images using optimal ellipse detection and texture maps. In: VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. 2015:329-332. Chapter 85. IFMBE Proceedings.
Li J, Wang Y, Lei B, et al. Automatic fetal head circumference measurement in ultrasound using random forest and fast ellipse fitting. IEEE J Biomed Health Inform. 2018;22(1):215-223. https://doi.org/10.1109/JBHI.2017.2703890
Foi A, Maggioni M, Pepe A, et al. Difference of Gaussians revolved along elliptical paths for ultrasound fetal head segmentation. Comput Med Imaging Graph. 2014;38(8):774-784. https://doi.org/10.1016/j.compmedimag.2014.09.006
Al-Bander B, Alzahrani T, Alzahrani S, Williams BM, Zheng Y. Improving Fetal Head Contour Detection by Object Localisation with Deep Learning. Springer International Publishing; 2020:142-150.
Fiorentino MC, Moccia S, Capparuccini M, Giamberini S, Frontoni E. A regression framework to head-circumference delineation from US fetal images. Comput Methods Progr Biomed. 2021;198:105771. https://doi.org/10.1016/j.cmpb.2020.105771
Moccia S, Fiorentino MC, Frontoni E. Mask-R[Formula: see text]CNN: a distance-field regression version of Mask-RCNN for fetal-head delineation in ultrasound images. Int J Comput Assist Radiol Surg. 2021;16(10):1711-1718. https://doi.org/10.1007/s11548-021-02430-0
Wang X, Wang W, Cai X. Automatic measurement of fetal head circumference using a novel GCN-assisted deep convolutional network. Comput Biol Med. 2022;145:105515. https://doi.org/10.1016/j.compbiomed.2022.105515
Zeng Y, Tsui PH, Wu W, Zhou Z, Wu S. Fetal ultrasound image segmentation for automatic head circumference biometry using deeply supervised attention-gated V-Net. J Digit Imag. 2021;34(1):134-148. https://doi.org/10.1007/s10278-020-00410-5
Zhang J, Petitjean C, Ainouz S. Segmentation-based vs. regression-based biomarker estimation: a case study of fetus head circumference assessment from ultrasound images. J Imaging. 2022;8(2):8. https://doi.org/10.3390/jimaging8020023
Sobhaninia Z, Rafiei S, Emami A, et al. Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. IEEE. 2019:6545-6548.
Zhang J, Petitjean C, Lopez P, Ainouz S. Direct estimation of fetal head circumference from ultrasound images based on regression CNN. In: Presented at: Proceedings of the Third Conference on Medical Imaging with Deep Learning; 2020. Proceedings of Machine Learning Research. Accessed December 12, 2022. https://proceedings.mlr.press/v121/zhang20a.html
Sun C. Automatic fetal head measurements from ultrasound images using circular shortest paths. n.d..
Rajinikanth V, Dey N, Kumar R, Panneerselvam J, Raja NSM. Fetal head periphery extraction from ultrasound image using Jaya algorithm and Chan-Vese segmentation. Procedia Comput Sci. 2019/01/01/ 2019;152:66-73. https://doi.org/10.1016/j.procs.2019.05.028
Burgos-Artizzu XP, Coronado-Gutierrez D, Valenzuela-Alcaraz B, 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(6):100462. https://doi.org/10.1016/j.ajogmf.2021.100462
Zhu F, Liu M, Wang F, Qiu D, Li R, Dai C. Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet. Math Biosci Eng. 2021;18(6):7790-7805. https://doi.org/10.3934/mbe.2021387
Hur H, Kim YH, Cho HY, et al. Feasibility of three-dimensional reconstruction and automated measurement of fetal long bones using 5D Long Bone. Obstet Gynecol Sci. 2015;58(4):268-276. https://doi.org/10.5468/ogs.2015.58.4.268
Wang CW. Automatic entropy-based femur segmentation and fast length measurement for fetal ultrasound images. 2014:1-5.
Shrimali V, Anand RS, Kumar V. Improved segmentation of ultrasound images for fetal biometry, using morphological operators. 2009:459-462.
Mukherjee P, Swamy G, Gupta M, Patil U, Krishnan KB. Automatic detection and measurement of femur length from fetal ultrasonography. In: Presented at: Medical Imaging 2010: Ultrasonic Imaging, Tomography, and Therapy. 2010.
Jang J, Park Y, Kim B, Lee SM, Kwon JY, Seo JK. Automatic estimation of fetal abdominal circumference from ultrasound images. IEEE J Biomed Health Inform. 2018;22(5):1512-1520. https://doi.org/10.1109/JBHI.2017.2776116
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(10):105007. https://doi.org/10.1088/1361-6579/aae255
Chen H, Ni D, Qin J, et al. Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J Biomed Health Inform. 2015;19(5):1627-1636. https://doi.org/10.1109/JBHI.2015.2425041
Chen H, Ni D, Yang X, Li S, Heng PA. Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer. Springer International Publishing; 2014:125-132.
Rahmatullah B, Sarris I, Papageorghiou A, Noble JA. Quality control of fetal ultrasound images: detection of abdomen anatomical landmarks using AdaBoost. 2011:6-9.
Wu L, Cheng JZ, Li S, Lei B, Wang T, Ni D. FUIQA: fetal ultrasound image quality assessment with deep convolutional networks. IEEE Trans Cybern. 2017;47(5):1336-1349. https://doi.org/10.1109/TCYB.2017.2671898
Ashkani Chenarlogh V, Ghelich Oghli M, Shabanzadeh A, et al. Fast and accurate U-Net model for fetal ultrasound image segmentation. Ultrason Imag. 2022;44(1):25-38. https://doi.org/10.1177/01617346211069882
Salim I, Cavallaro A, Ciofolo-Veit C, et al. Evaluation of automated tool for two-dimensional fetal biometry. Ultrasound Obstet Gynecol. 2019;54(5):650-654. https://doi.org/10.1002/uog.20185
Wu L, Xin Y, Li S, Wang T, Heng P-A, Ni D. Cascaded fully convolutional networks for automatic prenatal ultrasound image segmentation. In: Presented at: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). 2017.
Ponomarev GV, Gelfand MS, Kazanov MD. A multilevel thresholding combined with edge detection and shape-based recognition for segmentation of fetal ultrasound images. In: Proceedings of Challenge US: Biometric Measurements from Fetal Ultrasound Images, ISBI. 2012:17-19.
Miyagi Y, Miyake T. Potential of artificial intelligence for estimating Japanese fetal weights. Acta Med Okayama. 2020;74(6):483-493. https://doi.org/10.18926/amo/61207
Sakai A, Komatsu M, Komatsu R, et al. Medical professional enhancement using explainable artificial intelligence in fetal cardiac ultrasound screening. Biomedicines. 2022;10(3):10. https://doi.org/10.3390/biomedicines10030551
Abuhamad A, Falkensammer P, Reichartseder F, Zhao Y. Automated retrieval of standard diagnostic fetal cardiac ultrasound planes in the second trimester of pregnancy: a prospective evaluation of software. Ultrasound Obstet Gynecol. 2008;31(1):30-36. https://doi.org/10.1002/uog.5228
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(5):882-891. https://doi.org/10.1038/s41591-021-01342-5
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(6):853-863. https://doi.org/10.1002/uog.23703
Dozen A, Komatsu M, Sakai A, et al. Image segmentation of the ventricular septum in fetal cardiac ultrasound videos based on deep learning using time-series information. Biomolecules. 2020;10(11):10. https://doi.org/10.3390/biom10111526
Qiao S, Pan S, Luo G, et al. A pseudo-siamese feature fusion generative adversarial network for synthesizing high-quality fetal four-chamber views. IEEE J Biomed Health Inform. 2022;27(3):1193-1204. https://doi.org/10.1109/JBHI.2022.3143319
Qiao S, Pang S, Luo G, Pan S, Chen T, Lv Z. FLDS: an intelligent feature learning detection system for visualizing medical images supporting fetal four-chamber views. IEEE J Biomed Health Inform. 2017;26(10):4814-4825. https://doi.org/10.1109/JBHI.2021.3091579
Dong J, Liu S, Liao Y, et al. A generic quality control framework for fetal ultrasound cardiac four-chamber planes. IEEE J Biomed Health Inform. 2020;24(4):931-942. https://doi.org/10.1109/JBHI.2019.2948316
Sundaresan V, Bridge CP, Ioannou C, Noble JA. Automated Characterization of the Fetal Heart in Ultrasound Images Using Fully Convolutional Neural Networks. 2017:671-674.
Xi J, Chen J, Wang Z, et al. Simultaneous segmentation of fetal hearts and lungs for medical ultrasound images via an efficient multi-scale model integrated with attention mechanism. Ultrason Imag. 2021;43(6):308-319. https://doi.org/10.1177/01617346211042526
Di Vece C, Dromey B, Vasconcelos F, David AL, Peebles D, Stoyanov D. Deep learning-based plane pose regression in obstetric ultrasound. Int J Comput Assist Radiol Surg. 2022;17(5):833-839. https://doi.org/10.1007/s11548-022-02609-z
Namburete AIL, Xie W, Yaqub M, Zisserman A, Noble JA. Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning. Med Image Anal. 2018;46:1-14. https://doi.org/10.1016/j.media.2018.02.006
Skelton E, Matthew J, Li Y, et al. Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: a clinical evaluation and quality assessment comparison. Radiography. 2021;27(2):519-526. https://doi.org/10.1016/j.radi.2020.11.006
Hesse LS, Aliasi M, Moser F, et al. Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning. Neuroimage. 2022;254:119117. https://doi.org/10.1016/j.neuroimage.2022.119117
Huang R, Xie W, Alison Noble J. VP-Nets: efficient automatic localization of key brain structures in 3D fetal neurosonography. Med Image Anal. 2018;47:127-139. https://doi.org/10.1016/j.media.2018.04.004
Yaqub M, Kelly B, Papageorghiou AT, Noble JA. A deep learning solution for automatic fetal neurosonographic diagnostic plane verification using clinical standard constraints. Ultrasound Med Biol. 2017;43(12):2925-2933. https://doi.org/10.1016/j.ultrasmedbio.2017.07.013
Sofka M, Zhang J, Good S, Zhou SK, Comaniciu D. Automatic detection and measurement of structures in fetal head ultrasound volumes using sequential estimation and integrated detection network (IDN). IEEE Trans Med Imag. 2014;33(5):1054-1070. https://doi.org/10.1109/TMI.2014.2301936
Montero A, Bonet-Carne E, Burgos-Artizzu XP. Generative adversarial networks to improve fetal brain fine-grained plane classification. Sensors. 2021;29(23):21. https://doi.org/10.3390/s21237975
Qu R, Xu G, Ding C, Jia W, Sun M. Standard plane identification in fetal brain ultrasound scans using a differential convolutional neural network. IEEE Access. 2020;8:83821-83830. https://doi.org/10.1109/access.2020.2991845
Namburete AI, Stebbing RV, Kemp B, Yaqub M, Papageorghiou AT, Alison Noble J. Learning-based prediction of gestational age from ultrasound images of the fetal brain. Med Image Anal. 2015;21(1):72-86. https://doi.org/10.1016/j.media.2014.12.006
Alansary A, Oktay O, Li Y, et al. Evaluating reinforcement learning agents for anatomical landmark detection. Med Image Anal. 2019;53:156-164. https://doi.org/10.1016/j.media.2019.02.007
Chen X, He M, Dan T, et al. Automatic measurements of fetal lateral ventricles in 2D ultrasound images using deep learning. Front Neurol. 2020;11:526. https://doi.org/10.3389/fneur.2020.00526
Huang R, Namburete A, Noble A. Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor. J Med Imaging. 2018;5(1):014007. https://doi.org/10.1117/1.JMI.5.1.014007
Bullard KA, Shaffer BL, Greiner KS, Skeith AE, Rodriguez MI, Caughey AB. Twenty-week abortion bans on pregnancies with a congenital diaphragmatic hernia: a cost-effectiveness analysis. Obstet Gynecol. 2018;131(3):581-590. https://doi.org/10.1097/AOG.0000000000002483
Yaqub M, Napolitano R, Ioannou C, Papageorghiou AT, Noble JA. Automatic detection of local fetal brain structures in ultrasound images. In: Presented at: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). 2012.
Lin M, He X, Guo H, 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(3):304-316. https://doi.org/10.1002/uog.24843
Xie B, Lei T, Wang N, et al. Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks. Int J Comput Assist Radiol Surg. 2020;15(8):1303-1312. https://doi.org/10.1007/s11548-020-02182-3
Sahli H, Mouelhi A, Ben Slama A, Sayadi M, Rachdi R. Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images. J Med Eng Technol. 2019;43(5):279-286. https://doi.org/10.1080/03091902.2019.1653389
Namburete AIL, Noble JA. Fetal cranial segmentation in 2D ultrasound images using shape properties of pixel clusters. 2013:720-723.
Xie HN, Wang N, He M, et al. Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal. Ultrasound Obstet Gynecol. 2020;56(4):579-587. https://doi.org/10.1002/uog.21967
Matthew J, Skelton E, Day TG, et al. Exploring a new paradigm for the fetal anomaly ultrasound scan: artificial intelligence in real time. Prenat Diagn. 2022;42(1):49-59. https://doi.org/10.1002/pd.6059
Baumgartner CF, Kamnitsas K, Matthew J, et al. SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans Med Imag. 2017;36(11):2204-2215. https://doi.org/10.1109/TMI.2017.2712367
Sharma H, Droste R, Chatelain P, Drukker L, Papageorghiou AT, Noble JA. Spatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scans. Proc IEEE Int Symp Biomed Imaging. 2019;16:987-990. https://doi.org/10.1109/ISBI.2019.8759149
Schlemper J, Oktay O, Schaap M, et al. Attention gated networks: learning to leverage salient regions in medical images. Med Image Anal. 2019;53:197-207. https://doi.org/10.1016/j.media.2019.01.012
Sridar P, Kumar A, Quinton A, Nanan R, Kim J, Krishnakumar R. Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks. Ultrasound Med Biol. 2019;45(5):1259-1273. https://doi.org/10.1016/j.ultrasmedbio.2018.11.016
Chen H, Wu L, Dou Q, et al. Ultrasound standard plane detection using a composite neural network framework. IEEE Trans Cybern. 2017;47(6):1576-1586. https://doi.org/10.1109/TCYB.2017.2685080
Burgos-Artizzu XP, Perez-Moreno A, Coronado-Gutierrez D, Gratacos E, Palacio M. Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis. Sci Rep. 2019;9(1):1950. https://doi.org/10.1038/s41598-019-38576-w
Bonet-Carne E, Palacio M, Cobo T, et al. Quantitative ultrasound texture analysis of fetal lungs to predict neonatal respiratory morbidity. Ultrasound Obstet Gynecol. 2015;45(4):427-433. https://doi.org/10.1002/uog.13441
Cobo T, Bonet-Carne E, Martinez-Terron M, et al. Feasibility and reproducibility of fetal lung texture analysis by automatic quantitative ultrasound analysis and correlation with gestational age. Fetal Diagn Ther. 2012;31(4):230-236. https://doi.org/10.1159/000335349
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(15):1828-1837. https://doi.org/10.1097/CM9.0000000000001547
Jiao J, Droste R, Drukker L, Papageorghiou AT, Noble JA. Self-supervised representation learning for ultrasound video. Proc IEEE Int Symp Biomed Imaging. 2020;2020:1847-1850. https://doi.org/10.1109/ISBI45749.2020.9098666
Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D. Self-supervised learning for medical image analysis using image context restoration. Med Image Anal. 2019;58:101539. https://doi.org/10.1016/j.media.2019.101539
Yaqub M, Kelly B, Papageorghiou AT, Noble JA. Guided random forests for identification of key fetal anatomy and image categorization in ultrasound scans. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. 2015:687-694. Chapter 82. Lecture Notes in Computer Science.
Lei B, Tan EL, Chen S, et al. Automatic recognition of fetal facial standard plane in ultrasound image via fisher vector. PLoS One. 2015;10(5):e0121838. https://doi.org/10.1371/journal.pone.0121838
Zhen Y, Dong N, Siping C, Shengli L, Tianfu W, Baiying L. Fetal facial standard plane recognition via very deep convolutional networks. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:627-630. https://doi.org/10.1109/embc.2016.7590780
Cho HC, Sun S, Min Hyun C, et al. Automated ultrasound assessment of amniotic fluid index using deep learning. Med Image Anal. 2021;69:101951. https://doi.org/10.1016/j.media.2020.101951
Li Y, Xu R, Ohya J, Iwata H. Automatic fetal body and amniotic fluid segmentation from fetal ultrasound images by encoder-decoder network with inner layers. 2017:1485-1488.
Ghi T, Conversano F, Ramirez Zegarra R, et al. Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non-occiput anterior positions during labor. Ultrasound Obstet Gynecol. 2022;59(1):93-99. https://doi.org/10.1002/uog.23739
Du Y, Fang Z, Jiao J, 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(5):804-812. https://doi.org/10.1002/uog.22037
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(5):596-603. https://doi.org/10.1515/jpm-2020-0537
Ni D, Yang X, Chen X, et al. Standard plane localization in ultrasound by radial component model and selective search. Ultrasound Med Biol. 2014;40(11):2728-2742. https://doi.org/10.1016/j.ultrasmedbio.2014.06.006
Feng S, Zhou SK, Good S, Comaniciu D. Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information. 2009:2488-2495.
Maraci MA, Bridge CP, Napolitano R, Papageorghiou A, Noble JA. A framework for analysis of linear ultrasound videos to detect fetal presentation and heartbeat. Med Image Anal. 2017;37:22-36. https://doi.org/10.1016/j.media.2017.01.003
Shozu K, Komatsu M, Sakai A, et al. Model-agnostic method for thoracic wall segmentation in fetal ultrasound videos. Biomolecules. 2020;10(12):10. https://doi.org/10.3390/biom10121691
Pradipta GA, Wardoyo R, Musdholifah A, Sanjaya INH. Machine learning model for umbilical cord classification using combination coiling index and texture feature based on 2-D Doppler ultrasound images. Health Inf J. 2022;28(1):14604582221084211. https://doi.org/10.1177/14604582221084211
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. https://doi.org/10.3389/fped.2020.00001
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(6):2050-2057. https://doi.org/10.1109/JBHI.2020.3027318
Yang Y, Yang P, Zhang B. Automatic Segmentation in Fetal Ultrasound Images Based on Improved U-Net. IOP Publishing; 2020:012183.
Gomez A, Zimmer V, Toussaint N, et al. Image reconstruction in a manifold of image patches: application to whole-fetus ultrasound imaging. In: Machine Learning for Medical Image Reconstruction. 2019:226-235. Chapter 21. Lecture Notes in Computer Science.
Burgos-Artizzu XP, Coronado-Gutierrez D, Valenzuela-Alcaraz B, et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci Rep. 2020;10(1):10200. https://doi.org/10.1038/s41598-020-67076-5
Lei B, Zhuo L, Chen S, Li S, Ni D, Wang T. Automatic recognition of fetal standard plane in ultrasound image. 2014:85-88.
Bakker MK, Bergman JEH, Krikov S, et al. Prenatal diagnosis and prevalence of critical congenital heart defects: an international retrospective cohort study. BMJ Open. 2019;9(7):e028139. https://doi.org/10.1136/bmjopen-2018-028139
Holland BJ, Myers JA, Woods CR, Jr. Prenatal diagnosis of critical congenital heart disease reduces risk of death from cardiovascular compromise prior to planned neonatal cardiac surgery: a meta-analysis. Ultrasound Obstet Gynecol. 2015;45(6):631-638. https://doi.org/10.1002/uog.14882
Mahle WT, Clancy RR, McGaurn SP, Goin JE, Clark BJ. Impact of prenatal diagnosis on survival and early neurologic morbidity in neonates with the hypoplastic left heart syndrome. Pediatrics. 2001;107(6):1277-1282. https://doi.org/10.1542/peds.107.6.1277
Berhan Y. Predictors of perinatal mortality associated with placenta previa and placental abruption: an experience from a low income country. J Pregnancy. 2014;2014:307043. https://doi.org/10.1155/2014/307043
Vidaeff AC, Belfort MA, Kemp MW, et al. Updating the balance between benefits and harms of antenatal corticosteroids. Am J Obstet Gynecol. 2022;14(2):129-132. https://doi.org/10.1016/j.ajog.2022.10.002
Räikkönen K, Gissler M, Kajantie E. Associations between maternal antenatal corticosteroid treatment and mental and behavioral disorders in children. JAMA. 2020;323(19):1924-1933. https://doi.org/10.1001/jama.2020.3937