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Tytuł:
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Utilizing Grasp Monitoring to Predict Microsurgical Expertise.
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Autorzy:
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Koskinen J; School of Computing, University of Eastern Finland, Joensuu, Finland. Electronic address: .
He W; Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Elomaa AP; Department of Neurosurgery, Institute of Clinical Medicine, Kuopio University Hospital, Kuopio, Finland; Microsurgery Center, Kuopio University Hospital, Kuopio, Finland; School of Medicine, University of Eastern Finland, Kuopio, Finland.
Kaipainen A; Department of Neurosurgery, Institute of Clinical Medicine, Kuopio University Hospital, Kuopio, Finland; Microsurgery Center, Kuopio University Hospital, Kuopio, Finland.
Hussein A; Department of Neurosurgery, Institute of Clinical Medicine, Kuopio University Hospital, Kuopio, Finland; Microsurgery Center, Kuopio University Hospital, Kuopio, Finland.
Zheng B; Surgical Simulation Research Lab, Department of Surgery, University of Alberta, Edmonton, Alberta, Canada.
Huotarinen A; Department of Neurosurgery, Institute of Clinical Medicine, Kuopio University Hospital, Kuopio, Finland; Microsurgery Center, Kuopio University Hospital, Kuopio, Finland.
Bednarik R; School of Computing, University of Eastern Finland, Joensuu, Finland.
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Źródło:
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The Journal of surgical research [J Surg Res] 2023 Feb; Vol. 282, pp. 101-108. Date of Electronic Publication: 2022 Oct 18.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Publication: New York, NY : Academic Press
Original Publication: Philadelphia [etc.]
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MeSH Terms:
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Clinical Competence*
Surgeons*
Humans ; Sutures ; Microsurgery ; Hand Strength
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Contributed Indexing:
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Keywords: Eye tracking; Grasping; Microsurgery; Skill evaluation; Video analysis
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Entry Date(s):
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Date Created: 20221020 Date Completed: 20221129 Latest Revision: 20230216
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Update Code:
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20240105
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DOI:
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10.1016/j.jss.2022.09.018
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PMID:
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36265429
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Introduction: Most microsurgical procedures require the surgeon to use tools to grasp and hold fragile objects in the surgical site. Prior research on grasping in surgery has mostly either been in other surgical techniques or used grasping as an auxiliary metric. We focus on microsurgery and investigate what grasping can tell about microsurgical skill and suturing performance. This study lays groundwork for using automatic detection of grasps to evaluate surgical skill.
Methods: Five expert surgeons and six novices completed sutures on a microsurgical training board. Video recordings of the performance were annotated for the number of grasps, while an eye tracker recorded the participants' pupil dilations for cognitive workload assessment. Performance was measured with suturing duration and the University of Western Ontario Microsurgical Skills Assessment instrument (UWOMSA). Differences in skill, suturing performance and cognitive workload were compared with grasping behavior.
Results: Novices needed significantly more grasps to complete sutures and failed to grasp more often than the experts. The number of grasps affected the suturing duration more in novices. Decreasing suturing efficiency as measured by UWOMSA instrument was associated with increase in grasps, even when we controlled for overall skill differences. Novices displayed larger pupil dilations when averaged over a sufficiently large sample, and the difference increased after the grasp.
Conclusions: Grasping action during microsurgical procedures can be used as a conceptually simple yet objective proxy in microsurgical performance assessment. If the grasps could be detected automatically, they could be used to aid in computational evaluation of surgical trainees' performance.
(Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)
