|
|
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
| 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 |
| URI: | http://hdl.handle.net/2115/88926 |
| 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)
|
|
