{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:37:53Z","timestamp":1772642273569,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T00:00:00Z","timestamp":1556496000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T00:00:00Z","timestamp":1556496000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OISE-1065092"],"award-info":[{"award-number":["OISE-1065092"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005238","name":"Link Foundation","doi-asserted-by":"publisher","award":["90078471"],"award-info":[{"award-number":["90078471"]}],"id":[{"id":"10.13039\/100005238","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2019,11]]},"DOI":"10.1007\/s11548-019-01953-x","type":"journal-article","created":{"date-parts":[[2019,4,30]],"date-time":"2019-04-30T12:02:28Z","timestamp":1556625748000},"page":"2005-2020","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks"],"prefix":"10.1007","volume":"14","author":[{"given":"Robert","family":"DiPietro","sequence":"first","affiliation":[]},{"given":"Narges","family":"Ahmidi","sequence":"additional","affiliation":[]},{"given":"Anand","family":"Malpani","sequence":"additional","affiliation":[]},{"given":"Madeleine","family":"Waldram","sequence":"additional","affiliation":[]},{"given":"Gyusung I.","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Mija R.","family":"Lee","sequence":"additional","affiliation":[]},{"given":"S. Swaroop","family":"Vedula","sequence":"additional","affiliation":[]},{"given":"Gregory D.","family":"Hager","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,4,29]]},"reference":[{"key":"1953_CR1","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1109\/TBME.2016.2647680","volume":"64","author":"N Ahmidi","year":"2017","unstructured":"Ahmidi N, Tao L, Sefati S, Gao Y, Lea C, Haro BB, Zappella L, Khudanpur S, Vidal R, Hager GD (2017) A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery. IEEE Trans Biomed Eng 64:2025\u20132041","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"1953_CR2","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/j.surg.2009.06.044","volume":"146","author":"RH Bell","year":"2009","unstructured":"Bell RH (2009) Why Johnny cannot operate. Surgery 146(4):533\u2013542","journal-title":"Surgery"},{"issue":"2","key":"1953_CR3","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157\u2013166","journal-title":"IEEE Trans Neural Netw"},{"issue":"Feb","key":"1953_CR4","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(Feb):281\u2013305","journal-title":"J Mach Learn Res"},{"issue":"15","key":"1953_CR5","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1056\/NEJMsa1300625","volume":"369","author":"JD Birkmeyer","year":"2013","unstructured":"Birkmeyer JD, Finks JF, O\u2019reilly A, Oerline M, Carlin AM, Nunn AR, Dimick J, Banerjee M, Birkmeyer NJ (2013) Surgical skill and complication rates after bariatric surgery. N Engl J Med 369(15):1434\u20131442","journal-title":"N Engl J Med"},{"key":"1953_CR6","doi-asserted-by":"crossref","unstructured":"Cho K, van Merri\u00ebnboer B, G\u00fcl\u00e7ehre \u00c7, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. In: EMNLP","DOI":"10.3115\/v1\/D14-1179"},{"key":"1953_CR7","doi-asserted-by":"crossref","unstructured":"DiPietro R, Hager GD (2018) Unsupervised learning for surgical motion by learning to predict the future. In: International conference on medical image computing and computer-assisted intervention","DOI":"10.1007\/978-3-030-00937-3_33"},{"key":"1953_CR8","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1007\/978-3-319-46720-7_64","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"Robert DiPietro","year":"2016","unstructured":"DiPietro R, Lea C, Malpani A, Ahmidi N, Vedula SS, Lee GI, Lee MR, Hager GD (2016) Recognizing surgical activities with recurrent neural networks. In: International conference on medical image computing and computer-assisted intervention, pp 551\u2013558"},{"key":"1953_CR9","unstructured":"DiPietro R, Rupprecht C, Navab N, Hager GD (2017) Analyzing and exploiting NARX recurrent neural networks for long-term dependencies. arXiv preprint \narXiv:1702.07805"},{"issue":"2","key":"1953_CR10","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman JL (1990) Finding structure in time. Cognit Sci 14(2):179\u2013211","journal-title":"Cognit Sci"},{"issue":"10","key":"1953_CR11","doi-asserted-by":"publisher","first-page":"S70","DOI":"10.1097\/00001888-200410001-00022","volume":"79","author":"KA Ericsson","year":"2004","unstructured":"Ericsson KA (2004) Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. Acad Med 79(10):S70\u2013S81","journal-title":"Acad Med"},{"key":"1953_CR12","doi-asserted-by":"crossref","unstructured":"Gao Y, Vedula S, Lee GI, Lee MR, Khudanpur S, Hager GD (2016) Unsupervised surgical data alignment with application to automatic activity annotation. In: 2016 IEEE international conference on robotics and automation (ICRA)","DOI":"10.1109\/ICRA.2016.7487608"},{"key":"1953_CR13","unstructured":"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) Language of surgery: a surgical gesture dataset for human motion modeling. In: Modeling and monitoring of computer assisted interventions (M2CAI) 2014. Springer, Boston"},{"issue":"4","key":"1953_CR14","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.jsurg.2012.04.004","volume":"69","author":"SL Gearhart","year":"2012","unstructured":"Gearhart SL, Wang MH, Gilson MM, Chen B, Kern DE (2012) Teaching and assessing technical proficiency in surgical subspecialty fellowships. J Surg Educ 69(4):521\u2013528","journal-title":"J Surg Educ"},{"key":"1953_CR15","doi-asserted-by":"crossref","unstructured":"Gers FA, Schmidhuber J (2000) Recurrent nets that time and count. In: Neural networks, IJCNN, vol 3","DOI":"10.1109\/IJCNN.2000.861302"},{"issue":"10","key":"1953_CR16","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451\u20132471","journal-title":"Neural Comput"},{"key":"1953_CR17","unstructured":"Greff K, Srivastava RK, Koutn\u00edk J, Steunebrink BR, Schmidhuber J (2015) LSTM: a search space odyssey. arXiv preprint \narXiv:1503.04069"},{"issue":"1","key":"1953_CR18","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/S0925-2312(99)00174-5","volume":"31","author":"B Hammer","year":"2000","unstructured":"Hammer B (2000) On the approximation capability of recurrent neural networks. Neurocomputing 31(1):107\u2013123","journal-title":"Neurocomputing"},{"issue":"8","key":"1953_CR19","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"1953_CR20","unstructured":"Hutter F, Hoos H, Leyton-Brown K (2014) An efficient approach for assessing hyperparameter importance. In: International conference on machine learning, pp 754\u2013762"},{"key":"1953_CR21","unstructured":"Jacobs DM, Poenaru D (eds) (2001) Surgical educators\u2019 handbook. Association for Surgical Education, Los Angeles"},{"key":"1953_CR22","unstructured":"Lafferty J, McCallum A, Pereira FC (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. Technical report, UPenn"},{"key":"1953_CR23","doi-asserted-by":"crossref","unstructured":"Lea C, Hager GD, Vidal R (2015) An improved model for segmentation and recognition of fine-grained activities with application to surgical training tasks. In: 2015 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1123\u20131129","DOI":"10.1109\/WACV.2015.154"},{"key":"1953_CR24","doi-asserted-by":"crossref","unstructured":"Lea C, Vidal R, Hager GD (2016) Learning convolutional action primitives for fine-grained action recognition. In: 2016 IEEE international conference on robotics and automation (ICRA)","DOI":"10.1109\/ICRA.2016.7487305"},{"key":"1953_CR25","doi-asserted-by":"crossref","unstructured":"Lea C, Vidal R, Hager GD (2016) Learning convolutional action primitives from multimodal time series data. In: Proceedings of the IEEE international conference on robotics and automation\u2014ICRA","DOI":"10.1109\/ICRA.2016.7487305"},{"key":"1953_CR26","doi-asserted-by":"crossref","unstructured":"Lea C, Vidal R, Reiter A, Hager GD (2016) Temporal convolutional networks: a unified approach to action segmentation. In: European conference on computer vision. Springer, pp 47\u201354","DOI":"10.1007\/978-3-319-49409-8_7"},{"issue":"6","key":"1953_CR27","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1109\/72.548162","volume":"7","author":"T Lin","year":"1996","unstructured":"Lin T, Horne BG, Tino P, Giles CL (1996) Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans Neural Netw 7(6):1329\u20131338","journal-title":"IEEE Trans Neural Netw"},{"key":"1953_CR28","doi-asserted-by":"crossref","unstructured":"Liu D, Jiang T (2018) Deep reinforcement learning for surgical gesture segmentation and classification. In: International conference on medical image computing and computer-assisted intervention","DOI":"10.1007\/978-3-030-00937-3_29"},{"key":"1953_CR29","doi-asserted-by":"crossref","unstructured":"Mavroudi E, Bhaskara D, Sefati S, Ali H, Vidal R (2018) End-to-end fine-grained action segmentation and recognition using conditional random field models and discriminative sparse coding. In: 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1558\u20131567","DOI":"10.1109\/WACV.2018.00174"},{"issue":"2","key":"1953_CR30","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/5.18626","volume":"77","author":"LR Rabiner","year":"1989","unstructured":"Rabiner LR (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proc IEEE 77(2):257\u2013286","journal-title":"Proc IEEE"},{"issue":"11","key":"1953_CR31","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673\u20132681","journal-title":"IEEE Trans Signal Process"},{"issue":"2","key":"1953_CR32","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.jss.2008.02.014","volume":"147","author":"DJ Scott","year":"2008","unstructured":"Scott DJ, Cendan JC, Pugh CM, Minter RM, Dunnington GL, Kozar RA (2008) The changing face of surgical education: simulation as the new paradigm. J Surg Res 147(2):189\u2013193","journal-title":"J Surg Res"},{"key":"1953_CR33","unstructured":"Sefati S, Cowan NJ, Vidal R (2015) Learning shared, discriminative dictionaries for surgical gesture segmentation and classification. In: Modeling and monitoring of computer assisted interventions (M2CAI) 2015. Springer, Berlin"},{"key":"1953_CR34","volume-title":"An introduction to conditional random fields for relational learning","author":"C Sutton","year":"2006","unstructured":"Sutton C, McCallum A (2006) An introduction to conditional random fields for relational learning, vol 2. MIT Press, Cambridge"},{"key":"1953_CR35","doi-asserted-by":"crossref","unstructured":"Tao L, Elhamifar E, Khudanpur S, Hager GD, Vidal R (2012) Sparse hidden Markov models for surgical gesture classification and skill evaluation. In: International conference on information processing in computer-assisted interventions. Springer, pp 167\u2013177","DOI":"10.1007\/978-3-642-30618-1_17"},{"key":"1953_CR36","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/978-3-642-40760-4_43","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"Lingling Tao","year":"2013","unstructured":"Tao L, Zappella L, Hager GD, Vidal R (2013) Surgical gesture segmentation and recognition. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Medical image computing and computer-assisted intervention (MICCAI) 2013, Part III. LNCS, vol 8151. Springer, Berlin, pp 339\u2013346"},{"key":"1953_CR37","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1146\/annurev-bioeng-071516-044435","volume":"19","author":"SS Vedula","year":"2017","unstructured":"Vedula SS, Ishii M, Hager GD (2017) Objective assessment of surgical technical skill and competency in the operating room. Annu Rev Biomed Eng 19:301\u2013325","journal-title":"Annu Rev Biomed Eng"},{"issue":"12","key":"1953_CR38","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1111\/j.1365-2923.2009.03534.x","volume":"43","author":"E Wenghofer","year":"2009","unstructured":"Wenghofer E, Klass D, Abrahamowicz M, Dauphinee D, Jacques A, Smee S, Blackmore D, Winslade N, Reidel K, Bartman I, Tamblyn R (2009) Doctor scores on national qualifying examinations predict quality of care in future practice. Med Educ 43(12):1166\u20131173","journal-title":"Med Educ"},{"key":"1953_CR39","unstructured":"Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv preprint \narXiv:1409.2329"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-019-01953-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11548-019-01953-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-019-01953-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T21:43:23Z","timestamp":1589665403000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11548-019-01953-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,29]]},"references-count":39,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2019,11]]}},"alternative-id":["1953"],"URL":"https:\/\/doi.org\/10.1007\/s11548-019-01953-x","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,29]]},"assertion":[{"value":"6 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"They authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical standard"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}