Differentiating Operator Skill During Routine Fetal Ultrasound Scanning UsingProbe Motion Tracking

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.

Publication
23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020)

BibTex

@InProceedings{wang2020asmus,
       author = {Wang, Yipei and Droste, Richard and Jiao, Jianbo and Sharma, Harshita and Drukker, Lior and Papageorghiou, Aris T. and Noble, J. Alison},
        title = "{Differentiating Operator Skill During Routine Fetal Ultrasound Scanning UsingProbe Motion Tracking}",
    booktitle = {International Workshop on Advances in Simplifying Medical Ultrasound (ASMUS)},
         year = {2020}
}