Differentiating Operator Skill during Routine Fetal Ultrasound Scanning using Probe Motion Tracking.
Fetal ultrasound
Operator skill
Probe motion
Journal
Medical ultrasound, and preterm, perinatal and paediatric image analysis
Titre abrégé: Med Ultrasound Preterm Perinat Paediatr Image Anal (2020)
Pays: Switzerland
ID NLM: 101772240
Informations de publication
Date de publication:
Oct 2020
Oct 2020
Historique:
entrez:
26
10
2020
pubmed:
27
10
2020
medline:
27
10
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33103166
doi: 10.1007/978-3-030-60334-2_18
pmc: PMC7116256
mid: EMS96575
doi:
Types de publication
Journal Article
Langues
eng
Pagination
180-188Subventions
Organisme : European Research Council
ID : 694581
Pays : International
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