“…Unfortunately, in our experimental set, the probe was almost always occluded, thus it could not be recorded by the cameras. This is one limitation of optoelectronic devices, as already discussed in our investigation [2]. In contrast, Bell et al [3] used a different technology (electromagnetic tracking) that is free from this limitation but that can measure only a reduced number of markers.…”
mentioning
confidence: 83%
“…We thank Drs. Bell and Holden for their appreciation and valuable insights [1] about our recent work on the evaluation of sonographers' skills by means of hand motion analysis [2].…”
“…Unfortunately, in our experimental set, the probe was almost always occluded, thus it could not be recorded by the cameras. This is one limitation of optoelectronic devices, as already discussed in our investigation [2]. In contrast, Bell et al [3] used a different technology (electromagnetic tracking) that is free from this limitation but that can measure only a reduced number of markers.…”
mentioning
confidence: 83%
“…We thank Drs. Bell and Holden for their appreciation and valuable insights [1] about our recent work on the evaluation of sonographers' skills by means of hand motion analysis [2].…”
“…Most prior work in sonographer skill assessment using hand motion uses predefined features (path length, completion time, and points scanned, etc.) [16,21]. Due to safety and privacy reasons in the pregnancy scan room, we could not use cameras or electromagnetic motion trackers as in some works.…”
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 newlyqualified 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.
“…Competency-based training is the idea that learners advance based on the quality of the training, rather than the quantity of the training and is a domain that has developed significant traction over recent years 28,29. Methods of assessment have been demonstrated through Hand motion analysis (HMA),30–32 and global rating scales and other surgical-specific outcomes, but their article suggests that this application may be applied to robotic cleft surgery (Figure 1). Figure 1Hand motion analysis is used to provide an objective assessment of cleft surgery.…”
The field of robotic surgery is an exciting and growing field that has bolstered its way to become a mainstream application in a number of surgical disciplines. The application of robotic surgery in cleft surgery is novel and has captivated many with the benefit it provides: the slender and small arms with wrist articulation at the instrument tip; motion scaling; tremor elimination; and high fidelity, three-dimensional visualization make the robot a very attractive platform for use in confined spaces with small surgical targets. The story of the origin of robotic surgery in cleft surgery is an interesting one, and one that has arisen from other allied surgical specialities to render robotic cleft surgery as its own specialised field. A field that has coined its own terms and has demonstrated a number of applications for its use. This review details the origins of robotic cleft surgery, its evolution and its current status and elaborates on future directions to enhance its application.
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