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 >

Objective evaluation of laparoscopic surgical skills in wet lab training based on motion analysis and machine learning

Files in This Item:

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

Title: Objective evaluation of laparoscopic surgical skills in wet lab training based on motion analysis and machine learning
Authors: Ebina, Koki Browse this author
Abe, Takashige Browse this author →KAKEN DB
Hotta, Kiyohiko Browse this author →KAKEN DB
Higuchi, Madoka Browse this author
Furumido, Jun Browse this author
Iwahara, Naoya Browse this author
Kon, Masafumi Browse this author →KAKEN DB
Miyaji, Kou Browse this author
Shibuya, Sayaka Browse this author
Yan, Lingbo Browse this author
Komizunai, Shunsuke Browse this author
Kurashima, Yo Browse this author →KAKEN DB
Kikuchi, Hiroshi Browse this author →KAKEN DB
Matsumoto, Ryuji Browse this author →KAKEN DB
Osawa, Takahiro Browse this author →KAKEN DB
Murai, Sachiyo Browse this author
Tsujita, Teppei Browse this author →KAKEN DB
Sase, Kazuya Browse this author
Chen, Xiaoshuai Browse this author
Konno, Atsushi Browse this author
Shinohara, Nobuo Browse this author →KAKEN DB
Keywords: Laparoscopic surgery
Simulation training
Motion capture
Machine learning
Surgical education
Issue Date: 1-Aug-2022
Publisher: Springer
Journal Title: Langenbeck's archives of surgery
Volume: 407
Issue: 5
Start Page: 2123
End Page: 2132
Publisher DOI: 10.1007/s00423-022-02505-9
Abstract: Background Our aim was to build a skill assessment system, providing objective feedback to trainees based on the motion metrics of laparoscopic surgical instruments. Methods Participants performed tissue dissection around the aorta (tissue dissection task) and renal parenchymal closure (parenchymal-suturing task), using swine organs in a box trainer under a motion capture (Mocap) system. Two experts assessed the recorded movies, according to the formula of global operative assessment of laparoscopic skills (GOALS: score range, 5-25), and the mean scores were utilized as objective variables in the regression analyses. The correlations between mean GOALS scores and Mocap metrics were evaluated, and potential Mocap metrics with a Spearman's rank correlation coefficient value exceeding 0.4 were selected for each GOALS item estimation. Four regression algorithms, support vector regression (SVR), principal component analysis (PCA)-SVR, ridge regression, and partial least squares regression, were utilized for automatic GOALS estimation. Model validation was conducted by nested and repeated k-fold cross validation, and the mean absolute error (MAE) was calculated to evaluate the accuracy of each regression model. Results Forty-five urologic, 9 gastroenterological, and 3 gynecologic surgeons, 4 junior residents, and 9 medical students participated in the training. In both tasks, a positive correlation was observed between the speed-related parameters (e.g., velocity, velocity range, acceleration, jerk) and mean GOALS scores, with a negative correlation between the efficiencyrelated parameters (e.g., task time, path length, number of opening/closing operations) and mean GOALS scores. Among the 4 algorithms, SVR showed the highest accuracy in the tissue dissection task (MAE(median) = 2.2352), and PCA-SVR in the parenchymal-suturing task (MAE(median) = 1.2714), based on 100 iterations of the validation process of automatic GOALS estimation. Conclusion We developed a machine learning-based GOALS scoring system in wet lab training, with an error of approximately 1-2 points for the total score, and motion metrics that were explainable to trainees. Our future challenges are the further improvement of onsite GOALS feedback, exploring the educational benefit of our model and building an efficient training program.
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