Advertisement

Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12437)

Abstract

In this paper, we consider differentiating operator skill during fetal ultrasound scanning using probe motion tracking. We present a novel convolutional neural network-based deep learning framework to model ultrasound probe motion in order to classify operator skill levels, that is invariant to operators’ personal scanning styles. In this study, probe motion data during routine second-trimester fetal ultrasound scanning was acquired by operators of known experience levels (2 newly-qualified operators and 10 expert operators). The results demonstrate that the proposed model can successfully learn underlying probe motion features that distinguish operator skill levels during routine fetal ultrasound with 95% accuracy.

Keywords

Operator skill Probe motion Fetal ultrasound 

Notes

Acknowledgement

We acknowledge the ERC (ERC-ADG-2015 694581, project PULSE), EPSRC (EP/M013774/1, Project Seebibyte), and the NIHR Oxford Biomedical Research Centre.

References

  1. 1.
    Tsfresh: Time series feature extraction based on scalable hypothesis tests. https://tsfresh.readthedocs.io/en/latest/
  2. 2.
    Ahmidi, N., et al.: String motif-based description of tool motion for detecting skill and gestures in robotic surgery. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 26–33. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40811-3_4CrossRefGoogle Scholar
  3. 3.
    Ahmidi, N., Ishii, M., Fichtinger, G., Gallia, G.L., Hager, G.D.: An objective and automated method for assessing surgical skill in endoscopic sinus surgery using eye-tracking and tool-motion data. In: International Forum of Allergy & Rhinology, vol. 2, pp. 507–515. Wiley Online Library (2012)Google Scholar
  4. 4.
    Chen, H., et al.: Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J. Biomed. Health Inf. 19(5), 1627–1636 (2015)CrossRefGoogle Scholar
  5. 5.
    Cox, B., Beard, P.: Imaging techniques: super-resolution ultrasound. Nature 527(7579), 451 (2015)CrossRefGoogle Scholar
  6. 6.
    Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. arXiv preprint arXiv:1409.7495 (2014)
  7. 7.
    Hatala, R., Cook, D.A., Brydges, R., Hawkins, R.: Constructing a validity argument for the objective structured assessment of technical skills (OSATS): a systematic review of validity evidence. Adv. Health Sci. Educ. 20(5), 1149–1175 (2015)CrossRefGoogle Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  9. 9.
    Kumar, R., et al.: Assessing system operation skills in robotic surgery trainees. Inter. J. Med. Robot. Comput. Assist. Surg. 8(1), 118–124 (2012)CrossRefGoogle Scholar
  10. 10.
    Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  11. 11.
    Salomon, L., et al.: Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet. Gynecol. 37(1), 116–126 (2011)CrossRefGoogle Scholar
  12. 12.
    Salomon, L., et al.: ISUOG practice guidelines: performance of first-trimester fetal ultrasound scan. Ultrasound Obstet. Gynecol. Official J. Int. Soc. Ultrasound Obstet. Gynecol. 41(1), 102 (2013)CrossRefGoogle Scholar
  13. 13.
    University of Oxford: PULSE: Perception ultrasound by learning sonographic experience. https://www.eng.ox.ac.uk/pulse/
  14. 14.
    Vedula, S.S., Ishii, M., Hager, G.D.: Objective assessment of surgical technical skill and competency in the operating room. Annu. Rev. Biomed. Eng. 19, 301–325 (2017)CrossRefGoogle Scholar
  15. 15.
    Vrachnis, N., et al.: International society of ultrasound in obstetrics and gynecology (ISUOG)-the propagation of knowledge in ultrasound for the improvement of OB/GYN care worldwide: experience of basic ultrasound training in Oman. BMC Med. Educ. 19(1), 434 (2019)CrossRefGoogle Scholar
  16. 16.
    Zago, M., et al.: Educational impact of hand motion analysis in the evaluation of fast examination skills. Eur. J. Trauma Emerg. Surg. 1–8 (2019).  https://doi.org/10.1007/s00068-019-01112-6
  17. 17.
    Zappella, L., Béjar, B., Hager, G., Vidal, R.: Surgical gesture classification from video and kinematic data. Med. Image Anal. 17(7), 732–745 (2013)CrossRefGoogle Scholar
  18. 18.
    Zia, A., Essa, I.: Automated surgical skill assessment in RMIS training. Int. J. Comput. Assist. Radiol. Surg. 13(5), 731–739 (2018)CrossRefGoogle Scholar
  19. 19.
    Zia, A., Sharma, Y., Bettadapura, V., Sarin, E.L., Clements, M.A., Essa, I.: Automated assessment of surgical skills using frequency analysis. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 430–438. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24553-9_53CrossRefGoogle Scholar
  20. 20.
    Zia, A., et al.: Automated video-based assessment of surgical skills for training and evaluation in medical schools. Int. J. Comput. Assist. Radiol. Surg. 11(9), 1623–1636 (2016)CrossRefGoogle Scholar
  21. 21.
    Ziesmann, M.T., et al.: Validation of hand motion analysis as an objective assessment tool for the focused assessment with sonography for trauma examination. J. Trauma Acute Care Surg. 79(4), 631–637 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
  2. 2.Nuffield Department of Women’s and Reproductive HealthUniversity of OxfordOxfordUK

Personalised recommendations