HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Medicine / Faculty of Medicine >
Peer-reviewed Journal Articles, etc >

Automatic assessment of laparoscopic surgical skill competence based on motion metrics

Files in This Item:

The file(s) associated with this item can be obtained from the following URL:

Title: Automatic assessment of laparoscopic surgical skill competence based on motion metrics
Authors: Ebina, Koki Browse this author
Abe, Takashige Browse this author
Hotta, Kiyohiko Browse this author
Higuchi, Madoka Browse this author
Furumido, Jun Browse this author
Iwahara, Naoya Browse this author
Kon, Masafumi Browse this author
Miyaji, Kou Browse this author
Shibuya, Sayaka Browse this author
Lingbo, Yan Browse this author
Komizunai, Shunsuke Browse this author
Kurashima, Yo Browse this author
Kikuchi, Hiroshi Browse this author
Matsumoto, Ryuji Browse this author
Osawa, Takahiro Browse this author
Murai, Sachiyo Browse this author
Tsujita, Teppei Browse this author
Sase, Kazuya Browse this author
Chen, Xiaoshuai Browse this author
Konno, Atsushi Browse this author
Shinohara, Nobuo Browse this author →KAKEN DB
Issue Date: 2-Nov-2022
Publisher: PLOS
Journal Title: PLoS ONE
Volume: 17
Issue: 11
Start Page: e0277105
Publisher DOI: 10.1371/journal.pone.0277105
Abstract: The purpose of this study was to characterize the motion features of surgical devices associated with laparoscopic surgical competency and build an automatic skill-credential system in porcine cadaver organ simulation training. Participants performed tissue dissection around the aorta, dividing vascular pedicles after applying Hem-o-lok (tissue dissection task) and parenchymal closure of the kidney (suturing task). Movements of surgical devices were tracked by a motion capture (Mocap) system, and Mocap-metrics were compared according to the level of surgical experience (experts: >= 50 laparoscopic surgeries, intermediates: 10-49, novices: 0-9), using the Kruskal-Wallis test and principal component analysis (PCA). Three machine-learning algorithms: support vector machine (SVM), PCA-SVM, and gradient boosting decision tree (GBDT), were utilized for discrimination of the surgical experience level. The accuracy of each model was evaluated by nested and repeated k-fold cross-validation. A total of 32 experts, 18 intermediates, and 20 novices participated in the present study. PCA revealed that efficiency-related metrics (e.g., path length) significantly contributed to PC 1 in both tasks. Regarding PC 2, speed-related metrics (e.g., velocity, acceleration, jerk) of right-hand devices largely contributed to the tissue dissection task, while those of left-hand devices did in the suturing task. Regarding the three-group discrimination, in the tissue dissection task, the GBDT method was superior to the other methods (median accuracy: 68.6%). In the suturing task, SVM and PCA-SVM methods were superior to the GBDT method (57.4 and 58.4%, respectively). Regarding the two-group discrimination (experts vs. intermediates/novices), the GBDT method resulted in a median accuracy of 72.9% in the tissue dissection task, and, in the suturing task, the PCA-SVM method resulted in a median accuracy of 69.2%. Overall, the mocap-based credential system using machine-learning classifiers provides a correct judgment rate of around 70% (two-group discrimination). Together with motion analysis and wet-lab training, simulation training could be a practical method for objectively assessing the surgical competence of trainees.
Type: article
Appears in Collections:医学院・医学研究院 (Graduate School of Medicine / Faculty of Medicine) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Export metadata:

OAI-PMH ( junii2 , jpcoar_1.0 )

MathJax is now OFF:


 - Hokkaido University