Automated surgical OSATS prediction from videos
Biomedical Imaging（2014）
摘要
The assessment of surgical skills is an essential part of medical training. The prevalent manual evaluations by expert surgeons are time consuming and often their outcomes vary substantially from one observer to another. We present a video-based framework for automated evaluation of surgical skills based on the Objective Structured Assessment of Technical Skills (OSATS) criteria. We encode the motion dynamics via frame kernel matrices, and represent the motion granularity by texture features. Linear discriminant analysis is used to derive a reduced dimensionality feature space followed by linear regression to predict OSATS skill scores. We achieve statistically significant correlation (p-value <;0.01) between the ground-truth (given by domain experts) and the OSATS scores predicted by our framework.
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关键词
biomedical education,biomedical optical imaging,computer based training,image motion analysis,medical computing,surgery,video cameras,OSATS skill score prediction,Objective Structured Assessment of Technical Skills,automated surgical OSATS prediction,automated surgical skill evaluation,dimensionality feature space,domain expert-given ground-truth,expert surgeon manual evaluation,frame kernel matrices,framework-predicted OSATS scores,linear discriminant analysis,linear regression,manual evaluation outcome,medical training,motion granularity representation,prevalent manual evaluation,statistically significant correlation,surgical motion dynamics,surgical skill evaluation OSATS criteria,surgical skills assessment,texture features,time consuming manual evaluation,video-based surgical framework,video-based surgical skill evaluation,OSATS,motion texture,surgical skill,video analysis
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