{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:24:24Z","timestamp":1787318664941,"version":"build-2736575974"},"reference-count":36,"publisher":"Institution of Engineering and Technology (IET)","issue":"6","license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["GA863146"],"award-info":[{"award-number":["GA863146"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020194","name":"Wellcome \/ EPSRC Centre for Interventional and Surgical Sciences","doi-asserted-by":"publisher","award":["203145\/Z\/16\/Z"],"award-info":[{"award-number":["203145\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/501100020194","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020194","name":"Wellcome \/ EPSRC Centre for Interventional and Surgical Sciences","doi-asserted-by":"publisher","award":["NS\/A000050\/1"],"award-info":[{"award-number":["NS\/A000050\/1"]}],"id":[{"id":"10.13039\/501100020194","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/P012841\/1"],"award-info":[{"award-number":["EP\/P012841\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/S021612\/1"],"award-info":[{"award-number":["EP\/S021612\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/W00805X\/1"],"award-info":[{"award-number":["EP\/W00805X\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/Y01958X\/1"],"award-info":[{"award-number":["EP\/Y01958X\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Healthcare Tech Letters"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain\u2010specific expertise. Automated data\u2010driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models. However, these models are tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery. Here, a new public dataset is introduced: the nasal phase of simulated endoscopic pituitary surgery. Simulated surgery allows for a realistic yet repeatable environment, meaning the insights gained from automated assessment can be used by novice surgeons to hone their skills on the simulator before moving to real surgery. Pituitary Real\u2010time INstrument Tracking Network (PRINTNet) has been created as a baseline model for this automated assessment. Consisting of DeepLabV3 for classification and segmentation, StrongSORT for tracking, and the NVIDIA Holoscan for real\u2010time performance, PRINTNet achieved 71.9% multiple object tracking precision running at 22 frames per second. Using this tracking output, a multilayer perceptron achieved 87% accuracy in predicting surgical skill level (novice or expert), with the \u2018ratio of total procedure time to instrument visible time\u2019 correlated with higher surgical skill. The new publicly available dataset can be found at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.5522\/04\/26511049\">https:\/\/doi.org\/10.5522\/04\/26511049<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1049\/htl2.12101","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T07:25:46Z","timestamp":1733124346000},"page":"336-344","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automated surgical skill assessment in endoscopic pituitary surgery using real\u2010time instrument tracking on a high\u2010fidelity bench\u2010top phantom"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5682-9545","authenticated-orcid":false,"given":"Adrito","family":"Das","sequence":"first","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bilal","family":"Sidiqi","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurent","family":"Mennillo","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhehua","family":"Mao","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikael","family":"Brudfors","sequence":"additional","affiliation":[{"name":"NVIDIA London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Xochicale","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"},{"name":"School of Biomedical Engineering and Imaging Sciences King's College London London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyal Z.","family":"Khan","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"},{"name":"Department of Neurosurgery National Hospital for Neurology and Neurosurgery London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola","family":"Newall","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"},{"name":"Department of Neurosurgery National Hospital for Neurology and Neurosurgery London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John G.","family":"Hanrahan","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"},{"name":"Department of Neurosurgery National Hospital for Neurology and Neurosurgery London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew J.","family":"Clarkson","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"},{"name":"Department of Medical Physics and Biomedical Engineering University College London London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danail","family":"Stoyanov","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hani J.","family":"Marcus","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"},{"name":"Department of Neurosurgery National Hospital for Neurology and Neurosurgery London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophia","family":"Bano","sequence":"additional","affiliation":[{"name":"UCL Hawkes Institute University College London London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.5858\/2008-132-1231-PPPWDT"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.20412"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1210\/endrev\/bnad014"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11102-021-01162-3"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1227\/NEU.0b013e3181ef25c5"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1227\/NEU.0b013e31826d5d06"},{"issue":"2","key":"e_1_2_12_8_1","first-page":"273","article-title":"Objective structured assessment of technical skill (osats) for surgical residents: Objective structured assessment of technical skill","volume":"84","author":"Martin J.A.","year":"1997","journal-title":"Br. J. Surg."},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.3389\/fsurg.2022.1049685"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00595-012-0313-7"},{"key":"e_1_2_12_11_1","doi-asserted-by":"crossref","unstructured":"Khan D.Z. Koh C.H. Das A. Valetopolou A. Hanrahan J.G. Horsfall H.L. Baldeweg S.E. Bano S. Borg A. Dorward N.L. Olukoya O. Stoyanov D. Marcus H.J.:Video\u2010based performance analysis in pituitary surgery \u2010 part 1: Surgical outcomes. World Neurosurg. (2024).https:\/\/doi.org\/10.1016\/j.wneu.2024.07.218","DOI":"10.1016\/j.wneu.2024.07.218"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-023-10335-z"},{"key":"e_1_2_12_13_1","doi-asserted-by":"crossref","unstructured":"Khan D.Z. Newall N. Koh C.H. Das A. Aapan S. Horsfall H.L. Baldeweg S.E. Bano S. Borg A. Chari A. Dorward N.L. Elserius A. Giannis T. Jain A. Stoyanov D. Marcus H.J.:Video\u2010based performance analysis in pituitary surgery \u2010 part 2: Artificial intelligence assisted surgical coaching. World Neurosurg. (2024).https:\/\/doi.org\/10.1016\/j.wneu.2024.07.219","DOI":"10.1016\/j.wneu.2024.07.219"},{"key":"e_1_2_12_14_1","doi-asserted-by":"crossref","unstructured":"Gao Y. Vedula S.S. Reiley C.E. Narges A. Varadarajan B. Lin H.C. Tao L. Zappella L. Bejar B. Yuh D.D. Chen C.C.G. Vidal R. Khudanpur S. Hager G.D.:Jhu\u2010isi gesture and skill assessment working set (jigsaws): A surgical activity dataset for human motion modeling.Modeling and monitoring of computer assisted interventions (M2CAI)\u2013MICCAI Workshop(2014).https:\/\/doi.org\/10.1109\/TBME.2016.2647680","DOI":"10.1109\/TBME.2016.2647680"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-88175-x"},{"key":"e_1_2_12_16_1","unstructured":"Rivas\u2010Blanco I. P\u00e9rez\u2010del Pulgar C.J. Mariani A. Tortora G. Reina A.J.:A surgical dataset from the da vinci research kit for task automation and recognition. arXiv (2021).https:\/\/arxiv.org\/abs\/2102.03643"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-019-02073-2"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0267936"},{"key":"e_1_2_12_19_1","doi-asserted-by":"crossref","unstructured":"Das A. Khan D.Z. Psychogyios D. Zhang Y. Hanrahan J.G. Vasconcelos F. Pang Y. Chen Z. Wu J. Zou X. Zheng G. Qayyum A. Mazher M. Razzak I. Li T. Ye J. He J. P\u0142otka S. Kaleta J. Yamlahi A. Jund A. Godau P. Kondo S. Kasai S. Hirasawa K. Rivoir D. P\u00e9rez A. Rodriguez S. Arbel\u00e1ez P. Stoyanov D. Marcus H.J. Bano S.:Pitvis\u20102023 challenge: Workflow recognition in videos of endoscopic pituitary surgery. arXiv (2024).https:\/\/arxiv.org\/abs\/2409.01184","DOI":"10.1016\/j.media.2025.103716"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.107929"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2021.103945"},{"key":"e_1_2_12_22_1","doi-asserted-by":"crossref","unstructured":"Ronneberger O. Fischer P. Brox T.:U\u2010net: Convolutional networks for biomedical image segmentation. arXiv (2015).https:\/\/arxiv.org\/abs\/1505.04597","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_12_23_1","doi-asserted-by":"crossref","unstructured":"Bewley A. Ge Z. Ott L. Ramos F. Upcroft B.:Simple online and realtime tracking. In:2016 IEEE International Conference on Image Processing (ICIP).IEEE Piscataway NJ(2016)","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm9061964"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16449-1_52"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16449-1_39"},{"key":"e_1_2_12_27_1","unstructured":"He K. Zhang X. Ren S. Sun J.:Deep residual learning for image recognition. arXiv (2015).https:\/\/arxiv.org\/abs\/1512.03385"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-022-02599-y"},{"key":"e_1_2_12_29_1","article-title":"Automatic generation of operation notes in endoscopic pituitary surgery videos using workflow recognition","volume":"8","author":"Das A.","year":"2023","journal-title":"Intell.\u2010Based Med."},{"key":"e_1_2_12_30_1","unstructured":"Chen L.C. Papandreou G. Schroff F. Adam H.:Rethinking atrous convolution for semantic image segmentation. arXiv (2017).https:\/\/arxiv.org\/abs\/1706.05587"},{"key":"e_1_2_12_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43996-4_45"},{"key":"e_1_2_12_32_1","doi-asserted-by":"crossref","unstructured":"Wojke N. Bewley A. Paulus D.:Simple online and realtime tracking with a deep association metric. arXiv (2017).https:\/\/arxiv.org\/abs\/1703.07402","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"e_1_2_12_33_1","unstructured":"Du Y. Zhao Z. Song Y. Zhao Y. Su F. Gong T. Meng H.:Strongsort: Make deepsort great again. arXiv (2022). Available from:https:\/\/arxiv.org\/abs\/2202.13514"},{"key":"e_1_2_12_34_1","doi-asserted-by":"crossref","unstructured":"Sinha S. Dwivedi S. Azizian M.:Towards deterministic end\u2010to\u2010end latency for medical ai systems in nvidia holoscan. arXiv (2024).https:\/\/arxiv.org\/abs\/2402.04466","DOI":"10.1109\/ICCPS61052.2024.00028"},{"key":"e_1_2_12_35_1","first-page":"111","volume-title":"Can segformer be a true competitor to u\u2010net for medical image segmentation?","author":"Sourget T.","year":"2023"},{"key":"e_1_2_12_36_1","unstructured":"Xie E. Wang W. Yu Z. Anandkumar A. Alvarez J.M. Luo P.:Segformer: Simple and efficient design for semantic segmentation with transformers. arXiv (2021).https:\/\/arxiv.org\/abs\/2105.15203"},{"key":"e_1_2_12_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3250400"}],"container-title":["Healthcare Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl2.12101","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T06:08:50Z","timestamp":1761631730000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/htl2.12101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":36,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10.1049\/htl2.12101"],"URL":"https:\/\/doi.org\/10.1049\/htl2.12101","archive":["Portico"],"relation":{},"ISSN":["2053-3713","2053-3713"],"issn-type":[{"value":"2053-3713","type":"print"},{"value":"2053-3713","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"2024-11-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}