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)


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.


Operator skill Probe motion Fetal ultrasound 



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


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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

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