{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:30:31Z","timestamp":1777570231364,"version":"3.51.4"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006260","name":"Technion - Israel Institute of Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006260","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>In this paper, we present a novel approach to the automatic evaluation of open surgery skills using depth cameras. This work is intended to show that depth cameras achieve similar results to RGB cameras, which is the common method in the automatic evaluation of open surgery skills. Moreover, depth cameras offer advantages such as robustness to lighting variations, camera positioning, simplified data compression, and enhanced privacy, making them a promising alternative to RGB cameras.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Experts and novice surgeons completed two simulators of open suturing. We focused on hand and tool detection and action segmentation in suturing procedures. YOLOv8 was used for tool detection in RGB and depth videos. Furthermore, UVAST and MSTCN++ were used for action segmentation. Our study includes the collection and annotation of a dataset recorded with Azure Kinect.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We demonstrated that using depth cameras in object detection and action segmentation achieves comparable results to RGB cameras. Furthermore, we analyzed 3D hand path length, revealing significant differences between experts and novice surgeons, emphasizing the potential of depth cameras in capturing surgical skills. We also investigated the influence of camera angles on measurement accuracy, highlighting the advantages of 3D cameras in providing a more accurate representation of hand movements.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our research contributes to advancing the field of surgical skill assessment by leveraging depth cameras for more reliable and privacy evaluations. The findings suggest that depth cameras can be valuable in assessing surgical skills and provide a foundation for future research in this area.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-024-03158-3","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T11:02:03Z","timestamp":1715770923000},"page":"1349-1357","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Depth over RGB: automatic evaluation of open surgery skills using depth camera"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0432-4822","authenticated-orcid":false,"given":"Ido","family":"Zuckerman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicole","family":"Werner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Kouchly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emma","family":"Huston","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shannon","family":"DiMarco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"DiMusto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shlomi","family":"Laufer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"issue":"25","key":"3158_CR1","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1056\/NEJMra054785","volume":"355","author":"RK Reznick","year":"2006","unstructured":"Reznick RK, MacRae H (2006) Teaching surgical skills-changes in the wind. New Engl J Med 355(25):2664\u20132669","journal-title":"New Engl J Med"},{"issue":"3","key":"3158_CR2","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1001\/archsurg.140.3.293","volume":"140","author":"A Dosis","year":"2005","unstructured":"Dosis A, Aggarwal R, Bello F, Moorthy K, Munz Y, Gillies D, Darzi A (2005) Synchronized video and motion analysis for the assessment of procedures in the operating theater. Arch Surg 140(3):293\u2013299","journal-title":"Arch Surg"},{"key":"3158_CR3","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1007\/s004640080081","volume":"16","author":"S Smith","year":"2002","unstructured":"Smith S, Torkington J, Brown T, Taffinder N, Darzi A (2002) Motion analysis: a tool for assessing laparoscopic dexterity in the performance of a laboratory-based laparoscopic cholecystectomy. Surg Endosc 16:640\u2013645","journal-title":"Surg Endosc"},{"issue":"2","key":"3158_CR4","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1016\/j.amjsurg.2015.10.005","volume":"211","author":"A-LD D\u2019Angelo","year":"2016","unstructured":"D\u2019Angelo A-LD, Rutherford DN, Ray RD, Laufer S, Mason A, Pugh CM (2016) Working volume: validity evidence for a motion-based metric of surgical efficiency. Am J Surg 211(2):445\u2013450","journal-title":"Am J Surg"},{"issue":"3","key":"3158_CR5","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/s11548-022-02559-6","volume":"17","author":"A Goldbraikh","year":"2022","unstructured":"Goldbraikh A, D\u2019Angelo A-L, Pugh CM, Laufer S (2022) Video-based fully automatic assessment of open surgery suturing skills. Int J Comput Assist Radiol Surg 17(3):437\u2013448","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"3158_CR6","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.media.2018.05.001","volume":"47","author":"H Al Hajj","year":"2018","unstructured":"Al Hajj H, Lamard M, Conze P-H, Cochener B, Quellec G (2018) Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks. Med Image Anal 47:203\u2013218","journal-title":"Med Image Anal"},{"key":"3158_CR7","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1007\/s11548-019-01995-1","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. Int J Comput Assist Radiol Surg 14:1217\u20131225","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"3158_CR8","doi-asserted-by":"crossref","unstructured":"Fathabadi FR, Grantner JL, Shebrain SA, Abdel-Qader I (2021) Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149\u2013000154 . IEEE","DOI":"10.1109\/SAMI50585.2021.9378617"},{"key":"3158_CR9","doi-asserted-by":"crossref","unstructured":"Goldbraikh A, Avisdris N, Pugh CM, Laufer S (2022) Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406\u2013421 . Springer","DOI":"10.1007\/978-3-031-25066-8_22"},{"key":"3158_CR10","doi-asserted-by":"crossref","unstructured":"Halperin L, Sroka G, Zuckerman I, Laufer S (2023) Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1\u20134","DOI":"10.1007\/s11548-023-02963-6"},{"key":"3158_CR11","doi-asserted-by":"crossref","unstructured":"Bkheet E, D\u2019Angelo A-L, Goldbraikh A, Laufer S (2023) Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1\u20137","DOI":"10.1007\/s11548-023-02947-6"},{"issue":"5","key":"3158_CR12","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/j.enbuild.2008.11.023","volume":"41","author":"EG Dascalaki","year":"2009","unstructured":"Dascalaki EG, Gaglia AG, Balaras CA, Lagoudi A (2009) Indoor environmental quality in hellenic hospital operating rooms. Energy Build 41(5):551\u2013560. https:\/\/doi.org\/10.1016\/j.enbuild.2008.11.023","journal-title":"Energy Build"},{"issue":"6","key":"3158_CR13","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1080\/10790268.2017.1349856","volume":"40","author":"J Likitlersuang","year":"2017","unstructured":"Likitlersuang J, Sumitro ER, Theventhiran P, Kalsi-Ryan S, Zariffa J (2017) Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. J Spinal Cord Med 40(6):706\u2013714","journal-title":"J Spinal Cord Med"},{"issue":"7824","key":"3158_CR14","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1038\/s41586-020-2669-y","volume":"585","author":"A Haque","year":"2020","unstructured":"Haque A, Milstein A, Fei-Fei L (2020) Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824):193\u2013202","journal-title":"Nature"},{"issue":"3","key":"3158_CR15","doi-asserted-by":"publisher","first-page":"3200","DOI":"10.1109\/TPAMI.2022.3183112","volume":"45","author":"Z Sun","year":"2023","unstructured":"Sun Z, Ke Q, Rahmani H, Bennamoun M, Wang G, Liu J (2023) Human action recognition from various data modalities: a review. IEEE Trans Pattern Anal Mach Intell 45(3):3200\u20133225. https:\/\/doi.org\/10.1109\/TPAMI.2022.3183112","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"3158_CR16","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1038\/s41746-019-0087-z","volume":"2","author":"S Yeung","year":"2019","unstructured":"Yeung S, Rinaldo F, Jopling J, Liu B, Mehra R, Downing NL, Guo M, Bianconi GM, Alahi A, Lee J, Campbell B, Deru K, Beninati W, Fei-Fei L, Milstein A (2019) A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1):11","journal-title":"NPJ digital medicine"},{"key":"3158_CR17","doi-asserted-by":"crossref","unstructured":"Martinez-Martin N, Luo Z, Kaushal A, Adeli E, Haque A, Kelly SS, Wieten S, Cho MK, Magnus D, Fei-Fei L, Schulman K, Milstein A (2021) Ethical issues in using ambient intelligence in health-care settings. Lancet Digit Health 3(2):115\u2013123","DOI":"10.1016\/S2589-7500(20)30275-2"},{"key":"3158_CR18","doi-asserted-by":"publisher","first-page":"92300","DOI":"10.1109\/ACCESS.2021.3092403","volume":"9","author":"MH Siddiqi","year":"2021","unstructured":"Siddiqi MH, Almashfi N, Ali A, Alruwaili M, Alhwaiti Y, Alanazi S, Kamruzzaman M (2021) A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9:92300\u201392317","journal-title":"IEEE Access"},{"key":"3158_CR19","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1016\/j.jss.2020.07.051","volume":"256","author":"TP Williams","year":"2020","unstructured":"Williams TP, Snyder CL, Hancock KJ, Iglesias NJ, Sommerhalder C, DeLao SC, Chacin AC, Perez A (2020) Development of a low-cost, high-fidelity skin model for suturing. J Surg Res 256:618\u2013622","journal-title":"J Surg Res"},{"key":"3158_CR20","unstructured":"Buckarma E (2016) The how to book of low cost surgical simulation. https:\/\/surgicaleducation.mayo.edu\/how-to-book\/"},{"key":"3158_CR21","unstructured":"Biewald L (2020) Experiment tracking with weights and biases. Software available from https:\/\/www.wandb.com\/"},{"key":"3158_CR22","unstructured":"Jocher G, Chaurasia A, Qiu J YOLO by Ultralytics. https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"3158_CR23","doi-asserted-by":"crossref","unstructured":"Behrmann N, Golestaneh SA, Kolter Z, Gall J, Noroozi M (2022) Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European conference on computer vision, pp 52\u201368 . Springer","DOI":"10.1007\/978-3-031-19833-5_4"},{"key":"3158_CR24","doi-asserted-by":"publisher","first-page":"6647","DOI":"10.1109\/TPAMI.2020.3021756","volume":"45","author":"S-J Li","year":"2020","unstructured":"Li S-J, AbuFarha Y, Liu Y, Cheng M-M, Gall J (2020) Ms-tcn++: multi-stage temporal convolutional network for action segmentation. IEEE Trans Pattern Anal Mach Intell 45:6647\u20136658","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3158_CR25","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? A new model and the kinetics dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6299\u20136308","DOI":"10.1109\/CVPR.2017.502"},{"key":"3158_CR26","unstructured":"Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, Viola F, Green T, Back T, Natsev P, Suleyman M, Zisserman A (2017) The kinetics human action video dataset. arXiv preprint arXiv:1705.06950"},{"issue":"3","key":"3158_CR27","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1109\/PROC.1973.9030","volume":"61","author":"GD Forney","year":"1973","unstructured":"Forney GD (1973) The viterbi algorithm. Proc IEEE 61(3):268\u2013278","journal-title":"Proc IEEE"},{"key":"3158_CR28","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1007\/s00464-007-9526-z","volume":"22","author":"MK Chmarra","year":"2008","unstructured":"Chmarra MK, Jansen FW, Grimbergen CA, Dankelman J (2008) Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surg Endosc 22:943\u2013949","journal-title":"Surg Endosc"},{"key":"3158_CR29","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1007\/s11548-020-02259-z","volume":"15","author":"AK Lefor","year":"2020","unstructured":"Lefor AK, Harada K, Dosis A, Mitsuishi M (2020) Motion analysis of the JHU-ISI gesture and skill assessment working set using robotics video and motion assessment software. Int J Comput Assist Radiol Surg 15:2017\u20132025","journal-title":"Int J Comput Assist Radiol Surg"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03158-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-024-03158-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03158-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T04:07:43Z","timestamp":1726718863000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-024-03158-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,15]]},"references-count":29,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["3158"],"URL":"https:\/\/doi.org\/10.1007\/s11548-024-03158-3","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,15]]},"assertion":[{"value":"4 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2024","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":"This study was granted an exemption by the University of Wisconsin-Madison Institutional Review Board.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}