{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T13:00:40Z","timestamp":1789477240775,"version":"build-2803163510"},"reference-count":18,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JSPS Grant-in-Aid for Scientific Research","award":["JP17K08897"],"award-info":[{"award-number":["JP17K08897"]}]},{"name":"JSPS Grant-in-Aid for Scientific Research","award":["JP18H04102"],"award-info":[{"award-number":["JP18H04102"]}]},{"name":"Japan Keirin Auto race (JKA) Foundation","award":["2018M-156"],"award-info":[{"award-number":["2018M-156"]}]},{"name":"Grant-in-Aid for JSPS Fellows","award":["JP22J20957"],"award-info":[{"award-number":["JP22J20957"]}]},{"name":"JSPS Grant-in-Aid for Scientific Research","award":["JP21H00893"],"award-info":[{"award-number":["JP21H00893"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>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: \u226550 laparoscopic surgeries, intermediates: 10\u201349, novices: 0\u20139), 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0277105","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T13:58:31Z","timestamp":1667397511000},"page":"e0277105","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":28,"title":["Automatic assessment of laparoscopic surgical skill competence based on motion metrics"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4452-4274","authenticated-orcid":true,"given":"Koki","family":"Ebina","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5468-7632","authenticated-orcid":true,"given":"Takashige","family":"Abe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiyohiko","family":"Hotta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madoka","family":"Higuchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Furumido","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoya","family":"Iwahara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masafumi","family":"Kon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kou","family":"Miyaji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sayaka","family":"Shibuya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Lingbo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunsuke","family":"Komizunai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yo","family":"Kurashima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Kikuchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryuji","family":"Matsumoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takahiro","family":"Osawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sachiyo","family":"Murai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teppei","family":"Tsujita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuya","family":"Sase","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoshuai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsushi","family":"Konno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobuo","family":"Shinohara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"issue":"10","key":"pone.0277105.ref001","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1111\/iju.14315","article-title":"Development and validation of a porcine organ model for training in essential laparoscopic surgical skills","volume":"27","author":"M Higuchi","year":"2020","journal-title":"Int J Urol"},{"issue":"4","key":"pone.0277105.ref002","doi-asserted-by":"crossref","first-page":"191","DOI":"10.9746\/jcmsi.13.191","article-title":"Development and Validation of a Measurement System for Laparoscopic Surgical Procedures","volume":"13","author":"K Ebina","year":"2020","journal-title":"SICE JCMSI"},{"issue":"8","key":"pone.0277105.ref003","doi-asserted-by":"crossref","first-page":"4399","DOI":"10.1007\/s00464-020-07940-7","article-title":"Motion analysis for better understanding of psychomotor skills in laparoscopy: objective assessment-based simulation training using animal organs","volume":"35","author":"K Ebina","year":"2021","journal-title":"Surg Endosc"},{"issue":"2","key":"pone.0277105.ref004","first-page":"273","article-title":"Objective structured assessment of technical skill (OSATS) for surgical residents","volume":"84","author":"JA Martin","year":"1997","journal-title":"Br J Surg"},{"issue":"1","key":"pone.0277105.ref005","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.amjsurg.2005.04.004","article-title":"A global assessment tool for evaluation of intraoperative laparoscopic skills","volume":"190","author":"MC Vassiliou","year":"2005","journal-title":"Am J Surg"},{"issue":"1","key":"pone.0277105.ref006","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1007\/s00464-009-0556-6","article-title":"Support vector machines improve the accuracy of evaluation for the performance of laparoscopic training tasks","volume":"24","author":"B Allen","year":"2010","journal-title":"Surg Endosc"},{"issue":"8","key":"pone.0277105.ref007","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"A Savitzky","year":"1964","journal-title":"Anal. Chem"},{"key":"pone.0277105.ref008","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"F Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"pone.0277105.ref009","first-page":"3146","volume-title":"21 st Conference on Neural Information Processing Systems; 2017 Dec 4\u20139 Long Beach USA","author":"G Ke"},{"issue":"1","key":"pone.0277105.ref010","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s11548-019-02073-2","article-title":"Objective classification of psychomotor laparoscopic skills of surgeons based on three different approaches","volume":"15","author":"F Perez-Escamirosa","year":"2020","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"4","key":"pone.0277105.ref011","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1080\/13645706.2017.1284131","article-title":"Psychomotor skills assessment by motion analysis in minimally invasive surgery on an animal organ","volume":"26","author":"EF Hofstad","year":"2017","journal-title":"Minim Invasive Ther Allied Technol"},{"issue":"11","key":"pone.0277105.ref012","doi-asserted-by":"crossref","first-page":"3732","DOI":"10.1007\/s00464-019-06667-4","article-title":"Sensor-based machine learning for workflow detection and as key to detect expert level in laparoscopic suturing and knot-tying","volume":"33","author":"KF Kowalewski","year":"2019","journal-title":"Surg Endosc"},{"issue":"2","key":"pone.0277105.ref013","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1007\/s00464-013-3226-7","article-title":"Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis","volume":"28","author":"I Oropesa","year":"2014","journal-title":"Surg Endosc"},{"issue":"10","key":"pone.0277105.ref014","doi-asserted-by":"crossref","first-page":"3006","DOI":"10.1158\/1078-0432.CCR-18-3378","article-title":"Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers","volume":"25","author":"E Kawakami","year":"2019","journal-title":"Clin Cancer Res"},{"issue":"2","key":"pone.0277105.ref015","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1097\/RLI.0000000000000518","article-title":"Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients","volume":"54","author":"A Tahmassebi","year":"2019","journal-title":"Invest Radiol"},{"key":"pone.0277105.ref016","doi-asserted-by":"crossref","first-page":"105712","DOI":"10.1016\/j.cmpb.2020.105712","article-title":"Classifying the type of delivery from cardiotocographic signals: A machine learning approach","volume":"196","author":"C Ricciardi","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"pone.0277105.ref017","article-title":"Objective Evaluation of Laparoscopic Surgical Skills in Wet-lab Training Based on Motion Analysis and Machine Learning","author":"K Ebina","year":"2022","journal-title":"Langenbeck\u2019s Arch. Surg"},{"issue":"1","key":"pone.0277105.ref018","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.amjsurg.2005.04.004","article-title":"A global assessment tool for evaluation of intraoperative laparoscopic skills","volume":"190","author":"MC Vassiliou","year":"2005","journal-title":"Am J Surg"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0277105","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T13:59:08Z","timestamp":1667397548000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0277105"}},"subtitle":[],"editor":[{"given":"Jerritta","family":"Selvaraj","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,11,2]]},"references-count":18,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11,2]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0277105","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,2]]}}}