Donald School Journal of Ultrasound in Obstetrics and Gynecology

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VOLUME 15 , ISSUE 3 ( July-September, 2021 ) > List of Articles


Artificial Intelligence and Obstetric Ultrasound

Ryu Matsuoka

Keywords : Artificial intelligence, Computer-aided diagnosis, Convolutional neural network, Deep learning, Fetal ultrasound, Ovarian tumor

Citation Information : Matsuoka R. Artificial Intelligence and Obstetric Ultrasound. Donald School J Ultrasound Obstet Gynecol 2021; 15 (3):218-222.

DOI: 10.5005/jp-journals-10009-1702

License: CC BY-NC 4.0

Published Online: 30-09-2021

Copyright Statement:  Copyright © 2021; The Author(s).


Artificial intelligence (AI) technology is currently in its third era. Current AI technology is driven by machine learning (ML), particularly deep learning (DL). Deep learning is a computer technology that allows a computational model with multiple processing layers to learn the features of data. Convolutional neural networks have led to breakthroughs in the processing of images, videos, and audio. In medical imaging, computer-aided diagnosis algorithms for diabetic retinopathy, diabetic macular edema, tuberculosis, skin lesions, and colonoscopy classifiers are highly accurate and comparable to clinician performance. Although the application of AI technology in the field of ultrasound (US) has lagged behind other modalities such as radiography, computed tomography (CT), and magnetic resonance imaging (MRI), it has been rapidly applied in the field of obstetrics and gynecology in recent years. The results of AI processing of US images to determine the malignancy of ovarian tumors are comparable to the International Ovarian Tumor Analysis results, and it is now possible to identify each part of the body and calculate the estimated weight from fetal US movies. However, the application of AI to the central nervous system and especially to the fetal heart, which is the main part of fetal US morphological examination, is just beginning to progress.

  1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–444. DOI: 10.1038/nature14539.
  2. Akkus Z, Galimzianova A, Hoogi A, et al. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 2017;30(4):449–459. DOI: 10.1007/s10278-017-9983-4.
  3. LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten ZIP code recognition. Neural Comput 1989;1(4):541–551. DOI: 10.1162/neco.1989.1.4.541.
  4. Deng J, Dong W, Socher R, et al., ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition. Available at: Accessed June 18, 2019.
  5. Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis 2015;115(3):211–252. DOI: 10.1007/s11263-015-0816-y.
  6. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60–88. DOI: 10.1016/
  7. Winsberg F, Elkin M, Macy J, et al. Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis. Radiology 1967;89(2):211–215. DOI: 10.1148/89.2.211.
  8. Spiesberger W. Mammogram inspection by computer. IEEE. Trans. Biomed. Eng. 1979;26(4):213–219. DOI: 10.1109/tbme.1979.326560.
  9. Semmlow JL, Shadagopappan A, Ackerman LV, et al. A fully automated system for screening mammograms. Comp Biomed Res 1980;13(4):350–362. DOI: 10.1016/0010-4809(80)90027-0.
  10. Doi K. Chapter 1. Historical overview. In: Li Q, Nishikawa RM, ed. Computer-Aided Detection and Diagnosis in Medical Imaging. Boca Raton, FL: Taylor & Francis Group, LLC, CRC Press; 2015. pp. 1–17.
  11. Gulshan V, Peng L, Coram M, et al. Diabetic retinopathy in retinal fundus photographs. JAMA 2016;316(22):2402–2410. DOI: 10.1001/jama.2016.17216.
  12. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017;284(2):574–582. DOI: 10.1148/radiol.2017162326.
  13. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542(7639):115–118. DOI: 10.1038/nature21056.
  14. Mori Y, Kudo SE, Wakamura K, et al. Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos). Gastrointest Endosc 2015;81(3):621–629. DOI: 10.1016/j.gie.2014.09.008.
  15. Sharma A, Apostolidou S, Burnell M, et al. Risk of epithelial ovarian cancer in asymptomatic women with ultrasound-detected ovarian masses: a prospective cohort study within the UK collaborative trial of ovarian cancer screening (UKCTOCS). Ultrasound Obstet Gynecol 2012;40(3):338–344. DOI: 10.1002/uog.12270.
  16. Froyman W, Landolfo C, De Cock B, et al. Risk of complications in patients with conservatively managed ovarian tumours (IOTA5): a 2-year interim analysis of a multicentre, prospective, cohort study. Lancet Oncol 2019;20(3):448–458. DOI: 10.1016/S1470-2045(18)30837-4.
  17. Webb PM, Jordan SJ. Epidemiology of epithelial ovarian cancer. Best Pract Res Clin Obstet Gynaecol 2017;41:3–14. DOI: 10.1016/j.bpobgyn.2016.08.006.
  18. Timmerman D, Valentin L, Bourne TH, et al. Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the international ovarian tumor analysis (IOTA) group. Ultrasound Obstet Gynecol 2000;16(5):500–505. DOI: 10.1046/j.1469-0705.2000.00287.x.
  19. Froyman W, Timmerman D. Methods of assessing ovarian masses: international ovarian tumor analysis approach. Obstet Gynecol Clin North Am 2019;46(4):625–641. DOI: 10.1016/j.ogc.2019.07.003.
  20. Garg S, Kaur A, Mohi JK, et al. Evaluation of IOTA simple ultrasound rules to distinguish benign and malignant ovarian tumours. J Clin Diagn Res 2017;11(8):TC06–TC09. DOI: 10.7860/JCDR/2017/26790.10353.
  21. Khazendar S, Al-Assam H, Du H, et al., Automated classification of static ultrasound images of ovarian tumours based on decision level fusion. 6th Computer Science and Electronic Engineering Conference (CEEC), Colchester, UK, 2014; 148–153.
  22. Khazendar S, Sayasneh A, Al-Assam H, et al. Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator. Facts Views Vis Obgyn 2015;7(1):7–15.
  23. Christiansen F, Epstein EL, Smedberg E, et al. Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment. Ultrasound Obstet Gynecol 2021;57(1):155–163. DOI: 10.1002/uog.23530.
  24. 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. DOI: 10.1109/JBHI.2015.2425041.
  25. Kwitt R, Vasconcelos N, Razzaque S, et al. Localizing target structures in ultrasound video-a phantom study. Med Image Anal 2013;17(7):712–722. DOI: 10.1016/
  26. Lei B, Zhuo L, Chen S, et al., Automatic recognition of fetal standard plane in ultrasound image. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). Piscataway, NJ: IEEE; 2014. 85–88.
  27. Wu L, Cheng JZ, Li S, et al. FUIQA: fetal ultrasound image quality assessment with deep convolutional networks. IEEE Trans Cybern 2017;47(5):1336–1349. DOI: 10.1109/TCYB.2017.2671898.
  28. 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. DOI: 10.1109/TCYB.2017.2685080.
  29. Baumgartner CF, Kamnitsas K, Matthew J, et al. Sononet: Real-time detection and local-isation of fetal standard scan planes in freehand ultrasound. IEEE Trans Med Imaging 2017;36(11):2204–2215. DOI: 10.1109/TMI.2017.2712367.
  30. Ryou H, Yaqub M, Cavallaro A, et al. Automated 3D ultrasound image analysis for first trimester assessment of fetal health. Phys Med Biol 2019;64(18):185010. DOI: 10.1088/1361-6560/ab3ad1.
  31. Sridar P, Kumar A, Quinton A, et al. Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks. Ultrasound Med Biol 2019;45(5):1259–1273. DOI: 10.1016/j.ultrasmedbio.2018.11.016.
  32. Yu Z, Tan EL, Ni D, et al. A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition. IEEE J Biomed Health Inform 2018;22(3):874–885. DOI: 10.1109/JBHI.2017.2705031.
  33. Van den Heuvel TLA, Petros H, Santini S, et al. Ultrasound Med Biol 2019;45(3):773–785. DOI: 10.1016/j.ultrasmedbio.2018.09.015.
  34. Kim HP, Lee SM, Kwon JY, et al. Automatic evaluation of fetal head biometry from ultrasound images using machine learning. Physiol Meas 2019;40(6):065009. DOI: 10.1088/1361-6579/ab21ac.
  35. Malinger G, Paladini D, Haratz KK, et al. ISUOG practice guidelines (updated): sonographic examination of the fetal central nervous system. Part 1: performance of screening examination and indications for targeted neurosonography. Ultrasound Obstet Gynecol 2020;56(3):476–484. DOI: 10.1002/uog.22145.
  36. 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. DOI: 10.1002/uog.21967.
  37. Petrini JR, Broussard CS, Gilboa SM, et al. Racial differences by gestational age in neonatal deaths attributable to congenital heart defects—United States, 2003–2006. MMWR Morb Mortal Wkly Rep 2010;59:1208–1211.
  38. Wren C, Richmond S, Donaldson L. Temporal variability in birth prevalence of cardiovascular malformations. Heart 2000;83(4):414–419. DOI: 10.1136/heart.83.4.414.
  39. Meberg A, Otterstad JE, Froland G, et al. Outcome of congenital heart defects—a population-based study. Acta Paediatr 2000;89(11):1344–1351. DOI: 10.1080/080352500300002552.
  40. Holland BJ, Myers JA, Woods CR. Prenatal diagnosis of critical congenital heart disease reduces risk of death fromcardiovascular compromise prior to planned neonatal cardiac surgery: a meta-analysis. Ultrasound Obstet Gynecol 2015;45(6):631–638. DOI: 10.1002/uog.14882.
  41. Kirk JS, Riggs TW, Comstock CH, et al. Prenatal screening for cardiac anomalies; The value of routine addition of the aortic root to the four-chamber view. Obstet Gynecol 1994;84(3):427–431.
  42. DeVore GR. The aortic and pulmonary outflow tract screening examination in the human fetus. J Ultrasound Med 1992;11(7):345–348. DOI: 10.7863/jum.1992.11.7.345.
  43. Zhang YF, Zeng XL, Zhao EF, et al. Diagnostic value of fetal echocardiography for congenital heart disease: a systematic review and meta-analysis. Medicine (Baltimore) 2015;94(42):e1759. DOI: 10.1097/MD.0000000000001759.
  44. Rychik J, Ayres N, Cuneo B, et al. American society of echocardiography guidelines and standards for performance of the fetal echocardiogram. J Am Soc Echocardiogr 2004;17(7):803–810. DOI: 10.1016/j.echo.2004.04.011.
  45. van Velzen CL, Ket JCF, van de Ven PM, et al. Systematic review and meta-analysis of the performance of second-trimester screening for prenatal detection of congenital heart defects. Int J Gynaecol Obstet 2018;140(2):137–145. DOI: 10.1002/ijgo.12373.
  46. van Nisselrooij AEL, Teunissen AKK, Clur SA, et al. Why are congenital heart defects being missed? Ultrasound Obstet Gynecol 2020;55(6):747–757. DOI: 10.1002/uog.20358.
  47. Yeo L, Romero R. Fetal intelligent navigation echocardiography (FINE): a novel method for rapid, simple, and automatic examination of the fetal heart. Ultrasound Obstet Gynecol 2013;42(3):268–284. DOI: 10.1002/uog.12563.
  48. Arnaout R, Curran L, Zhao Y, et al. Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning. medRxiv 2020.
  49. Bridge CP, Ioannou C, Noble JA. Automated annotation and quantitative description of ultrasound videos of the fetal heart. Med Image Anal 2017;36:147–161. DOI: 10.1016/
  50. Noble JA, Boukerroui D. Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 2006;25(8):987–1010. DOI: 10.1109/tmi.2006.877092.
  51. Brattain LJ, Telfer BA, Dhyani M, et al. Machine learning for medical ultrasound: status, methods, and future opportunities. Abdom Radiol 2018;43(4):786–799. DOI: 10.1007/s00261-018-1517-0.
  52. Liu S, Wang Y, Yang X, et al. Deep learning in medical ultrasound analysis: a review. Engineering 2019;5(2):261–275. DOI: 10.1016/j.eng.2018.11.020.
  53. Yasutomi S, Arakaki T, Matsuoka R, et al. Shadow estimation for ultrasound images using auto-encoding structures and synthetic shadows. Appl Sci 2021;11(3):1127. DOI: 10.3390/app11031127.
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