Buia A, Stockhausen F, Hanisch E (2015) Laparoscopic surgery: a qualified systematic review. World J Methodol 5:238. https://doi.org/10.5662/wjm.v5.i4.238

Article  PubMed  PubMed Central  Google Scholar 

Menhadji A, Abdelshehid C, Osann K, Alipanah R, Lusch A, Graversen J, Lee J, Quach S, Huynh V, Sidhom D, Gerbatsch I, Landman J, McDougall E (2013) Tracking and assessment of technical skills acquisition among urology residents for open, laparoscopic, and robotic skills over 4 years: is there a trend? J Endourol 27:783–789. https://doi.org/10.1089/end.2012.0633

Article  PubMed  Google Scholar 

Vassiliou MC, Feldman LS, Andrew CG, Bergman S, Leffondré K, Stanbridge D, Fried GM (2005) A global assessment tool for evaluation of intraoperative laparoscopic skills. Am J Surg 190:107–113. https://doi.org/10.1016/j.amjsurg.2005.04.004

Article  PubMed  Google Scholar 

Fried GM, Feldman LS, Vassiliou MC, Fraser SA, Stanbridge D, Ghitulescu G, Andrew CG (2004) Proving the value of simulation in laparoscopic surgery. Ann Surg 240:518–528

Article  PubMed  PubMed Central  Google Scholar 

Sroka G, Feldman LS, Vassiliou MC, Kaneva PA, Fayez R, Fried GM (2010) Fundamentals of laparoscopic surgery simulator training to proficiency improves laparoscopic performance in the operating room—a randomized controlled trial. Am J Surg 199:115–120

Article  PubMed  Google Scholar 

Committee NMECE, of Laparoscopic Technical Evaluation, (2021) Chinese laparoscopic skills testing and assessment (CLSTA). Chin J Pract Surg 41:997–1001

Google Scholar 

Khalid S, Goldenberg M, Grantcharov T, Taati B, Rudzicz F (2020) Evaluation of deep learning models for identifying surgical actions and measuring performance. JAMA Netw Open 3:e201664. https://doi.org/10.1001/jamanetworkopen.2020.1664

Article  PubMed  Google Scholar 

Chen Z, An J, Wu S, Cheng K, You J, Liu J, Jiang J, Yang D, Peng B, Wang X (2022) Surgesture: a novel instrument based on surgical actions for objective skill assessment. Surg Endosc 36:6113–6121. https://doi.org/10.1007/s00464-022-09108-x

Article  PubMed  Google Scholar 

Kitaguchi D, Takeshita N, Matsuzaki H, Takano H, Owada Y, Enomoto T, Oda T, Miura H, Yamanashi T, Watanabe M, Sato D, Sugomori Y, Hara S, Ito M (2020) Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg Endosc 34:4924–4931. https://doi.org/10.1007/s00464-019-07281-0

Article  PubMed  Google Scholar 

Meeuwsen FC, Van Luyn F, Blikkendaal MD, Jansen FW, Van Den Dobbelsteen JJ (2019) Surgical phase modelling in minimal invasive surgery. Surg Endosc 33:1426–1432. https://doi.org/10.1007/s00464-018-6417-4

Article  CAS  PubMed  Google Scholar 

Van Amsterdam B, Clarkson MJ, Stoyanov D (2021) Gesture recognition in robotic surgery: a review. IEEE Trans Biomed Eng 68:2021–2035. https://doi.org/10.1109/TBME.2021.3054828

Article  PubMed  Google Scholar 

Kawka M, Gall TMh, Fang C, Liu R, Jiao LR (2022) Intraoperative video analysis and machine learning models will change the future of surgical training. Intell Surg 1:13–15. https://doi.org/10.1016/j.isurg.2021.03.001

Article  Google Scholar 

Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. 2015 IEEE international conference on computer vision (ICCV). IEEE, Santiago, pp 4489–4497

Chapter  Google Scholar 

Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35:221–231. https://doi.org/10.1109/TPAMI.2012.59

Article  PubMed  Google Scholar 

Hara K, Kataoka H, Satoh Y (2017) Learning spatio-temporal features with 3D residual networks for action recognition. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). IEEE, Venice, pp 3154–3160

J Carreira A Zisserman 2017 Quo vadis, action recognition? A new model and the kinetics dataset 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Honolulu, pp 4724 4733

Shi X, Chen Z, Wang H, Yeung D-Y, Wong W, Woo W (2015) Convolutional LSTM Network: a machine learning approach for precipitation nowcasting. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1. MIT Press, Cambridge, MA, USA, pp 802–810

Mao A, Mohri M, Zhong Y (2023) Cross-entropy loss functions: theoretical analysis and applications. In: Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J (eds) Proceedings of the 40th International Conference on Machine Learning. PMLR, pp 23803–23828

Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. https://doi.org/10.48550/arXiv.1412.6980

Funke I, Bodenstedt S, Oehme F, von Bechtolsheim F, Weitz J, Speidel S (2019) Using 3D convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video. In: Shen D et al (eds) Medical image computing and computer-assisted intervention – MICCAI 2019. Lecture Notes in Computer Science. Springer, Cham

Google Scholar 

Kumar R (2019) Machine learning quick reference. Packt Publishing, Birmingham

Google Scholar 

Garrow CR, Kowalewski K-F, Li L, Wagner M, Schmidt MW, Engelhardt S, Hashimoto DA, Kenngott HG, Bodenstedt S, Speidel S, Müller-Stich BP, Nickel F (2021) Machine learning for surgical phase recognition: a systematic review. Ann Surg 273:684–693. https://doi.org/10.1097/SLA.0000000000004425

Article  PubMed  Google Scholar 

Cheng K, You J, Wu S, Chen Z, Zhou Z, Guan J, Peng B, Wang X (2022) Artificial intelligence-based automated laparoscopic cholecystectomy surgical phase recognition and analysis. Surg Endosc 36:3160–3168. https://doi.org/10.1007/s00464-021-08619-3

Article  PubMed  Google Scholar 

Nwoye CI, Mutter D, Marescaux J, Padoy N (2019) Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos. Int J Comput Assist Radiol Surg 14:1059–1067. https://doi.org/10.1007/s11548-019-01958-6

Article  PubMed  Google Scholar 

Klank U, Padoy N, Feussner H, Navab N (2008) Automatic feature generation in endoscopic images. Int J Comput Assist Radiol Surg 3:331–339. https://doi.org/10.1007/s11548-008-0223-8

Article  Google Scholar 

Varban OA, Thumma JR, Finks JF, Carlin AM, Ghaferi AA, Dimick JB (2021) Evaluating the effect of surgical skill on outcomes for laparoscopic sleeve gastrectomy: a video-based study. Ann Surg 273:766–771. https://doi.org/10.1097/SLA.0000000000003385

Article  PubMed  Google Scholar 

Gao Y, Vedula SS, Reiley CE, Ahmidi N, Varadarajan B, Lin HC, Tao L, Zappella L, Bejar B, Yuh DD, Chen CCG, Vidal R, Khudanpur S, Hager GD (2014) JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS): a surgical activity dataset for human motion modeling. In: MICCAI workshop: M2cai, vol. 3, p 3

Zappella L, Béjar B, Hager G, Vidal R (2013) Surgical gesture classification from video and kinematic data. Med Image Anal 17:732–745. https://doi.org/10.1016/j.media.2013.04.007

Article  PubMed  Google Scholar 

Loukas C, Georgiou E (2011) Multivariate autoregressive modeling of hand kinematics for laparoscopic skills assessment of surgical trainees. IEEE Trans Biomed Eng 58:3289–3297. https://doi.org/10.1109/TBME.2011.2167324

Article  PubMed  Google Scholar 

Aggarwal R, Grantcharov T, Moorthy K, Milland T, Papasavas P, Dosis A, Bello F, Darzi A (2007) An evaluation of the feasibility, validity, and reliability of laparoscopic skills assessment in the operating room. Ann Surg 245:992–999. https://doi.org/10.1097/01.sla.0000262780.17950.e5

Article  PubMed  PubMed Central  Google Scholar 

Bouget D, Allan M, Stoyanov D, Jannin P (2017) Vision-based and marker-less surgical tool detection and tracking: a review of the literature. Med Image Anal 35:633–654. https://doi.org/10.1016/j.media.2016.09.003

Article  PubMed  Google Scholar 

Scally CP, Varban OA, Carlin AM, Birkmeyer JD, Dimick JB, Michigan Bariatric Surgery Collaborative (2016) Video ratings of surgical skill and late outcomes of bariatric surgery. JAMA Surg 151(6):e160428. https://doi.org/10.1001/jamasurg.2016.0428

Article  PubMed  PubMed Central  Google Scholar 

Martin JA, Regehr G, Reznick R, Macrae H, Murnaghan J, Hutchison C, Brown M (1997) Objective structured assessment of technical skill (OSATS) for surgical residents: objective structured assessment of technical skill. Br J Surg 84:273–278. https://doi.org/10.1046/j.1365-2168.1997.02502.x

Article  CAS  PubMed  Google Scholar 

Blikkendaal MD, Driessen SRC, Rodrigues SP, Rhemrev JPT, Smeets MJGH, Dankelman J, van den Dobbelsteen JJ, Jansen FW (2017) Surgical flow disturbances in dedicated minimally invasive surgery suites: an observational study to assess its supposed superiority over conventional suites. Surg Endosc 31:288–298. https://doi.org/10.1007/s00464-016-4971-1

Article  PubMed  Google Scholar 

Loukas C, Gazis A, Kanakis MA (2020) Surgical performance analysis and classification based on video annotation of laparoscopic tasks. J Soc Laparosc Robotic Surg 24(4):e2020-00057

Google Scholar