{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T01:51:39Z","timestamp":1771379499696,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,7,10]],"date-time":"2021-07-10T00:00:00Z","timestamp":1625875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,10]],"date-time":"2021-07-10T00:00:00Z","timestamp":1625875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["18511105603"],"award-info":[{"award-number":["18511105603"]}],"id":[{"id":"10.13039\/501100003399","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":[[2021,9]]},"DOI":"10.1007\/s11548-021-02448-4","type":"journal-article","created":{"date-parts":[[2021,7,10]],"date-time":"2021-07-10T11:02:35Z","timestamp":1625914955000},"page":"1595-1605","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Towards accurate and interpretable surgical skill assessment: a video-based method for skill score prediction and guiding feedback generation"],"prefix":"10.1007","volume":"16","author":[{"given":"Tianyu","family":"Wang","sequence":"first","affiliation":[]},{"given":"Minhao","family":"Jin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5184-5861","authenticated-orcid":false,"given":"Mian","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,10]]},"reference":[{"issue":"9","key":"2448_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(9):2025\u20132041","journal-title":"IEEE Trans Biomed Eng"},{"key":"2448_CR2","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 NJO (2013) Surgical skill and complication rates after bariatric surgery. N Engl J Med 369:1434\u20131442","journal-title":"N Engl J Med"},{"key":"2448_CR3","doi-asserted-by":"crossref","unstructured":"Farha YA, Gall J (2019) MS-TCN: Multi-stage temporal convolutional network for action segmentation. In: CVPR, pp. 3575\u20133584. IEEE","DOI":"10.1109\/CVPR.2019.00369"},{"key":"2448_CR4","doi-asserted-by":"crossref","unstructured":"Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller PA (2018) Evaluating surgical skills from kinematic data using convolutional neural networks. MICCAI, LNCS. vol 11073, pp 214\u2013221. Springer, Cham","DOI":"10.1007\/978-3-030-00937-3_25"},{"issue":"7","key":"2448_CR5","first-page":"1217","volume":"14","author":"I Funke","year":"2019","unstructured":"Funke I, Mees ST, Weitz J, Speidel S (2019) Video-based surgical skill assessment using 3D convolutional neural networks. IJCARS 14(7):1217\u20131225","journal-title":"IJCARS"},{"key":"2448_CR6","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR, pp 770\u2013778. IEEE","DOI":"10.1109\/CVPR.2016.90"},{"key":"2448_CR7","first-page":"1157","volume":"15","author":"K Iyengar","year":"2020","unstructured":"Iyengar K, Dwyer G, Stoyanov D (2020) Investigating exploration for deep reinforcement learning of concentric tube robot control. IJCARS 15:1157\u20131165","journal-title":"IJCARS"},{"key":"2448_CR8","unstructured":"Kendall A, Gal Y, Cipolla R (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: CVPR, pp 7482\u20137491. IEEE"},{"key":"2448_CR9","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22:79\u201386","journal-title":"Ann Math Stat"},{"key":"2448_CR10","doi-asserted-by":"crossref","unstructured":"Liu D, Jiang T (2018) Deep reinforcement learning for surgical gesture segmentation and classification. MICCAI, LNCS. vol 11073, pp 247\u2013255. Springer, Cham","DOI":"10.1007\/978-3-030-00937-3_29"},{"key":"2448_CR11","doi-asserted-by":"crossref","unstructured":"Liu D, Jiang T, Wang Y, Miao R, Shan F, Li Z (2019) Surgical skill assessment on in-vivo clinical data via the clearness of operating field. MICCAI, LNCS. vol 11768, pp 476\u2013484. Springer, Cham","DOI":"10.1007\/978-3-030-32254-0_53"},{"issue":"2","key":"2448_CR12","first-page":"273","volume":"84","author":"JA Martin","year":"1997","unstructured":"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. Br J Surg 84(2):273\u2013278","journal-title":"Br J Surg"},{"key":"2448_CR13","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: NIPS, pp 3111\u20133119"},{"key":"2448_CR14","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1016\/j.asoc.2014.08.054","volume":"24","author":"L Napalkova","year":"2014","unstructured":"Napalkova L, Rozenblit JW, Hwang G, Hamilton AJ, Suantak L (2014) An optimal motion planning method for computer-assisted surgical training. Appl Soft Comput 24:889\u2013899","journal-title":"Appl Soft Comput"},{"key":"2448_CR15","doi-asserted-by":"crossref","unstructured":"Parmar P, Morris BT (2017) Learning to score olympic events. In: CVPR-W, pp 20\u201328. IEEE","DOI":"10.1109\/CVPRW.2017.16"},{"key":"2448_CR16","doi-asserted-by":"crossref","unstructured":"Parmar P, Morris BT (2019) Action quality assessment across multiple actions. In: WACV, pp 1468\u20131476. IEEE","DOI":"10.1109\/WACV.2019.00161"},{"key":"2448_CR17","doi-asserted-by":"crossref","unstructured":"Parmar P, Morris BT (2019) What and how well you performed? A multitask learning approach to action quality assessment. In: CVPR, pp 304\u2013313. IEEE","DOI":"10.1109\/CVPR.2019.00039"},{"key":"2448_CR18","unstructured":"Sutton RS, McAllester DA, Singh SP, Mansour Y (2000) Policy gradient methods for reinforcement learning with function approximation. In: NIPS, pp 1057\u20131063"},{"key":"2448_CR19","first-page":"341","volume":"15","author":"X Tan","year":"2020","unstructured":"Tan X, Lee Y, Chng C, Lim K, Chui C (2020) Robot-assisted flexible needle insertion using universal distributional deep reinforcement learning. IJCARS 15:341\u2013349","journal-title":"IJCARS"},{"key":"2448_CR20","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: ICCV, pp 4489\u20134497. IEEE","DOI":"10.1109\/ICCV.2015.510"},{"key":"2448_CR21","doi-asserted-by":"publisher","unstructured":"Wang T, Wang Y, Li M (2020) Towards accurate and interpretable surgical skill assessment: A video-based method incorporating recognized surgical gestures and skill levels. In: MICCAI 2020, LNCS, vol 12263, pp 668\u2013678. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-59716-0_64","DOI":"10.1007\/978-3-030-59716-0_64"},{"issue":"12","key":"2448_CR22","first-page":"1959","volume":"13","author":"Z Wang","year":"2018","unstructured":"Wang Z, Fey AM (2018) Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. IJCARS 13(12):1959\u20131970","journal-title":"IJCARS"},{"key":"2448_CR23","doi-asserted-by":"crossref","unstructured":"Xiang X, Tian Y, Reiter A, Hager GD, Tran TD (2018) S3D: Stacking segmental P3D for action quality assessment. In: ICIP, pp 928\u2013932. IEEE","DOI":"10.1109\/ICIP.2018.8451364"},{"key":"2448_CR24","doi-asserted-by":"crossref","unstructured":"Zhou K, Qiao Y, Xiang T (2018) Deep reinforcement learning for unsupervised video summarization with diversity-representativeness reward. In: AAAI","DOI":"10.1609\/aaai.v32i1.12255"},{"issue":"5","key":"2448_CR25","first-page":"731","volume":"13","author":"A Zia","year":"2018","unstructured":"Zia A, Essa I (2018) Automated surgical skill assessment in RMIS training. IJCARS 13(5):731\u2013739","journal-title":"IJCARS"},{"issue":"3","key":"2448_CR26","first-page":"443","volume":"13","author":"A Zia","year":"2018","unstructured":"Zia A, Sharma Y, Bettadapura V, Sarin EL, Essa I (2018) Video and accelerometer-based motion analysis for automated surgical skills assessment. IJCARS 13(3):443\u2013455","journal-title":"IJCARS"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02448-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02448-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02448-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T16:59:03Z","timestamp":1672765143000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02448-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,10]]},"references-count":26,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["2448"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02448-4","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,10]]},"assertion":[{"value":"14 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict 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 approval"}},{"value":"This articles does not contain patient data.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The codes used during the current study are available at","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}