{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:46:01Z","timestamp":1791927961996,"version":"4.3.4"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5R01HL146843"],"award-info":[{"award-number":["5R01HL146843"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Global Surg Educ"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Open surgery training is vital in vascular surgery education. Traditionally, trainees\u2019 open surgical suturing skills are evaluated by their educators, which is a subjective and time-consuming process. This paper evaluates image-based metrics for surgical suturing skill assessment by comparing performance among three groups: attending surgeons, resident surgeons, and novices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A simulator, named SutureCoach, is developed to simulate a radial suturing task, performed either at \u201cdepth,\u201d representing a cavity, or at \u201csurface.\u201d Among other sensors, the simulator includes cameras and computer vision software to track the motion of the needle and thread under the membrane being sutured. A set of image-based metrics are calculated for each suture based on video data. The set of metrics includes four previously proposed metrics as well as four new metrics inspired by a refined suturing phase segmentation scheme. To evaluate the performance of the image-based metrics, we conducted a study with 97 participants, divided almost evenly between attending surgeons, resident surgeons, and novices. Data was collected at several events over a 5-month period.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The statistical analysis shows that all metrics have significantly different means when comparing performance between novices and surgeons at both conditions, surface and depth. Furthermore, seven out of eight metrics have significant mean differences when comparing resident surgeons\u2019 and attending surgeons\u2019 performance at the depth condition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Image-based metrics are capable of suturing skill assessment, especially for distinguishing novices from surgeons. Depth constraints allow higher skill levels to be better differentiated.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s44186-026-00596-2","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:00:34Z","timestamp":1791925234000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Developing and evaluating image-based metrics for open surgical suturing skill assessment"],"prefix":"10.1007","volume":"5","author":[{"given":"Jianxin","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simar P.","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir Mehdi","family":"Shayan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John F.","family":"Eidt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joe","family":"Bible","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph Vincent Valentino","family":"Blas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sagar S.","family":"Gandhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravikiran","family":"Singapogu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6994-821X","authenticated-orcid":false,"given":"Richard E.","family":"Groff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"issue":"4","key":"596_CR1","first-page":"781","volume":"95","author":"MG Sheahan","year":"2015","unstructured":"Sheahan MG, Duran C, Bismuth J. National simulation-based training of fellows: the vascular surgery example. Surg Clinics. 2015;95(4):781\u201390.","journal-title":"Surg Clinics"},{"key":"596_CR2","doi-asserted-by":"crossref","unstructured":"Mitchell EL, Sheahan MG, Schwiesow M. Simulation in vascular surgery. In: Comprehensive healthcare simulation: surgery and surgical subspecialties. 2019. pp. 327\u201347.","DOI":"10.1007\/978-3-319-98276-2_26"},{"issue":"3","key":"596_CR3","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1016\/j.jvs.2016.08.107","volume":"65","author":"KM Bennett","year":"2017","unstructured":"Bennett KM, Kent KC, Schumacher J, Greenberg CC, Scarborough JE. Targeting the most important complications in vascular surgery. J Vasc Surg. 2017;65(3):793\u2013803.","journal-title":"J Vasc Surg"},{"issue":"3","key":"596_CR4","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1097\/01.SAP.0000037271.26659.F4","volume":"50","author":"MS Khan","year":"2003","unstructured":"Khan MS, Bann S, Darzi A, Butler P. Use of suturing as a measure of technical competence. Ann Plast Surg. 2003;50(3):304\u20139.","journal-title":"Ann Plast Surg"},{"issue":"6","key":"596_CR5","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.jsurg.2021.07.009","volume":"78","author":"IS Schmiederer","year":"2021","unstructured":"Schmiederer IS, Kearse LE, Korndorffer JR Jr, Lee E, Sgroi MD, Lee JT. Validity evidence for vascular skills assessment: The feasibility of fundamentals of vascular surgery in general surgery residency. J Surg Educ. 2021;78(6):201\u20139.","journal-title":"J Surg Educ"},{"issue":"5","key":"596_CR6","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/j.amjsurg.2007.02.003","volume":"193","author":"JD Doyle","year":"2007","unstructured":"Doyle JD, Webber EM, Sidhu RS. A universal global rating scale for the evaluation of technical skills in the operating room. Am J Surg. 2007;193(5):551\u20135.","journal-title":"Am J Surg"},{"key":"596_CR7","doi-asserted-by":"crossref","unstructured":"Zia A, Sharma Y, Bettadapura V, Sarin EL, Clements MA, Essa I. Automated assessment of surgical skills using frequency analysis. In: medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Springer;2015. pp. 430\u2013438.","DOI":"10.1007\/978-3-319-24553-9_53"},{"key":"596_CR8","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s00595-012-0313-7","volume":"43","author":"H Niitsu","year":"2013","unstructured":"Niitsu H, Hirabayashi N, Yoshimitsu M, Mimura T, Taomoto J, Sugiyama Y, et al. Using the objective structured assessment of technical skills (osats) global rating scale to evaluate the skills of surgical trainees in the operating room. Surg Today. 2013;43:271\u20135.","journal-title":"Surg Today"},{"issue":"2","key":"596_CR9","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/S0002-9610(98)00327-4","volume":"177","author":"DJ Anastakis","year":"1999","unstructured":"Anastakis DJ, Regehr G, Reznick RK, Cusimano M, Murnaghan J, Brown M, et al. Assessment of technical skills transfer from the bench training model to the human model. Am J Surg. 1999;177(2):167\u201370.","journal-title":"Am J Surg"},{"issue":"2","key":"596_CR10","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.jvs.2006.09.040","volume":"45","author":"RS Sidhu","year":"2007","unstructured":"Sidhu RS, Park J, Brydges R, MacRae HM, Dubrowski A. Laboratory-based vascular anastomosis training: a randomized controlled trial evaluating the effects of bench model fidelity and level of training on skill acquisition. J Vasc Surg. 2007;45(2):343\u20139.","journal-title":"J Vasc Surg"},{"issue":"5","key":"596_CR11","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/S1072-7515(01)01041-9","volume":"193","author":"V Datta","year":"2001","unstructured":"Datta V, Mackay S, Mandalia M, Darzi A. The use of electromagnetic motion tracking analysis to objectively measure open surgical skill in the laboratory-based model. J Am Coll Surg. 2001;193(5):479\u201385.","journal-title":"J Am Coll Surg"},{"key":"596_CR12","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1007\/s00268-002-6769-7","volume":"27","author":"SD Bann","year":"2003","unstructured":"Bann SD, Khan MS, Darzi AW. Measurement of surgical dexterity using motion analysis of simple bench tasks. World J Surg. 2003;27:390\u20134.","journal-title":"World J Surg"},{"issue":"1","key":"596_CR13","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/S0002-9610(02)00891-7","volume":"184","author":"V Datta","year":"2002","unstructured":"Datta V, Chang A, Mackay S, Darzi A. The relationship between motion analysis and surgical technical assessments. Am J Surg. 2002;184(1):70\u20133.","journal-title":"Am J Surg"},{"issue":"1","key":"596_CR14","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.amjsurg.2005.04.006","volume":"190","author":"A Dubrowski","year":"2005","unstructured":"Dubrowski A, Sidhu R, Park J, Carnahan H. Quantification of motion characteristics and forces applied to tissues during suturing. Am J Surg. 2005;190(1):131\u20136.","journal-title":"Am J Surg"},{"key":"596_CR15","unstructured":"Singapogu RB, Kavathekar T, Eidt JF, Groff RE, Burg TC. A novel platform for assessment of surgical suturing skill: Preliminary results, MMVR; 2016. pp. 375\u20138."},{"key":"596_CR16","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1007\/s00464-010-1251-3","volume":"25","author":"S Yamaguchi","year":"2011","unstructured":"Yamaguchi S, Yoshida D, Kenmotsu H, Yasunaga T, Konishi K, Ieiri S, et al. Objective assessment of laparoscopic suturing skills using a motion-tracking system. Surg Endosc. 2011;25:771\u20135.","journal-title":"Surg Endosc"},{"key":"596_CR17","doi-asserted-by":"crossref","unstructured":"Uemura M, Tomikawa M, Miao T, Souzaki R, Ieiri S, Akahoshi T, et al. Feasibility of an ai-based measure of the hand motions of expert and novice surgeons. Computational and mathematical methods in medicine. 2018.","DOI":"10.1155\/2018\/9873273"},{"issue":"2","key":"596_CR18","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1109\/TMRB.2021.3073209","volume":"3","author":"Y Sharon","year":"2021","unstructured":"Sharon Y, Jarc AM, Lendvay TS, Nisky I. Rate of orientation change as a new metric for robot-assisted and open surgical skill evaluation. IEEE Trans Med Robot Bionics. 2021;3(2):414\u201325.","journal-title":"IEEE Trans Med Robot Bionics"},{"issue":"5","key":"596_CR19","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1016\/j.surg.2023.07.007","volume":"174","author":"AM Shayan","year":"2023","unstructured":"Shayan AM, Singh S, Gao J, Groff RE, Bible J, Eidt JF, et al. Measuring hand movement for suturing skill assessment: a simulation-based study. Surgery. 2023;174(5):1184\u201392.","journal-title":"Surgery"},{"key":"596_CR20","doi-asserted-by":"crossref","unstructured":"Shayan AM, Hitchcock DB, Singh S, Gao J, Groff RE, Singapogu RB. Functional data analysis of hand rotation for open surgical suturing skill assessment. IEEE J Biomed Health Inform. 2024","DOI":"10.1109\/JBHI.2024.3496122"},{"key":"596_CR21","doi-asserted-by":"crossref","unstructured":"Singh SP, Shayan AM, Gao J, Bible J, Groff RE, Singapogu R. Objective and automated quantification of instrument handling for open surgical suturing skill assessment: A simulation-based study. IEEE Open J Eng Med Biol. 2024","DOI":"10.36227\/techrxiv.170723611.12261495\/v1"},{"key":"596_CR22","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1007\/s00464-014-3442-9","volume":"28","author":"AL Trejos","year":"2014","unstructured":"Trejos AL, Patel RV, Malthaner RA, Schlachta CM. Development of force-based metrics for skills assessment in minimally invasive surgery. Surg Endosc. 2014;28:2106\u201319.","journal-title":"Surg Endosc"},{"key":"596_CR23","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1007\/s004640000230","volume":"14","author":"C Richards","year":"2000","unstructured":"Richards C, Rosen J, Hannaford B, Pellegrini C, Sinanan M. Skills evaluation in minimally invasive surgery using force\/torque signatures. Surg Endosc. 2000;14:791\u20138.","journal-title":"Surg Endosc"},{"key":"596_CR24","doi-asserted-by":"publisher","first-page":"3102","DOI":"10.1007\/s00464-010-1096-9","volume":"24","author":"T Horeman","year":"2010","unstructured":"Horeman T, Rodrigues SP, Jansen F-W, Dankelman J, Dobbelsteen JJ. Force measurement platform for training and assessment of laparoscopic skills. Surg Endosc. 2010;24:3102\u20138.","journal-title":"Surg Endosc"},{"key":"596_CR25","doi-asserted-by":"crossref","unstructured":"Singh SP, Shayan AM, Gao J, Biblev J, Groff RE, Singapogu R. Evaluating smoothness of force for surgical skill assessment. In: 2024 46th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2024. pp. 1\u20134.","DOI":"10.1109\/EMBC53108.2024.10781655"},{"issue":"2","key":"596_CR26","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1097\/ACM.0b013e31827c3411","volume":"88","author":"AC Frischknecht","year":"2013","unstructured":"Frischknecht AC, Kasten SJ, Hamstra SJ, Perkins NC, Gillespie RB, Armstrong TJ, et al. The objective assessment of experts\u2019 and novices\u2019 suturing skills using an image analysis program. Acad Med. 2013;88(2):260\u20134.","journal-title":"Acad Med"},{"key":"596_CR27","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.jbi.2015.11.002","volume":"59","author":"G Islam","year":"2016","unstructured":"Islam G, Kahol K, Li B, Smith M, Patel VL. Affordable, web-based surgical skill training and evaluation tool. J Biomed Inform. 2016;59:102\u201314.","journal-title":"J Biomed Inform"},{"key":"596_CR28","unstructured":"Islam G, Kahol K, Li B. Development of a computer vision application for surgical skill training and assessment. Bio-informatic systems processing and applications. 2013; 191."},{"issue":"1","key":"596_CR29","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s11548-019-02073-2","volume":"15","author":"F P\u00e9rez-Escamirosa","year":"2020","unstructured":"P\u00e9rez-Escamirosa F, Alarc\u00f3n-Paredes A, Alonso-Silverio GA, Oropesa I, Camacho-Nieto O, Lorias-Espinoza D, et al. Objective classification of psychomotor laparoscopic skills of surgeons based on three different approaches. Int J Comput Assist Radiol Surg. 2020;15(1):27\u201340.","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"596_CR30","doi-asserted-by":"crossref","unstructured":"Gao J, Shayan AM, Singh SP, Bible J, Singapogu R, Groff RE. Surgical suturing skill assessment using estimated hand roll angle from a deep-learning computer vision algorithm. In: 2024 46th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2024. pp. 1\u20136.","DOI":"10.1109\/EMBC53108.2024.10782514"},{"key":"596_CR31","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. Video-based surgical skill assessment using 3d convolutional neural networks. Int J Comput Assist Radiol Surg. 2019;14:1217\u201325.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"3","key":"596_CR32","doi-asserted-by":"publisher","first-page":"201664","DOI":"10.1001\/jamanetworkopen.2020.1664","volume":"3","author":"S Khalid","year":"2020","unstructured":"Khalid S, Goldenberg M, Grantcharov T, Taati B, Rudzicz F. Evaluation of deep learning models for identifying surgical actions and measuring performance. JAMA Netw Open. 2020;3(3):201664\u2013201664.","journal-title":"JAMA Netw Open"},{"key":"596_CR33","doi-asserted-by":"crossref","unstructured":"Liu D, Li Q, Jiang T, Wang Y, Miao R, Shan F, Li Z. Towards unified surgical skill assessment. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021. pp. 9522\u20139531.","DOI":"10.1109\/CVPR46437.2021.00940"},{"key":"596_CR34","doi-asserted-by":"crossref","unstructured":"Soleymani A, Asl AAS, Yeganejou M, Dick S, Tavakoli M, Li X. Surgical skill evaluation from robot-assisted surgery recordings. In: 2021 International symposium on medical robotics (ISMR). IEEE; 2021. pp. 1\u20136.","DOI":"10.1109\/ISMR48346.2021.9661527"},{"key":"596_CR35","doi-asserted-by":"crossref","unstructured":"Wang Y, Dai J, Morgan TN, Elsaied M, Garbens A, Qu X, et al. Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks. J Robot Surg. 2021;1\u20139.","DOI":"10.1007\/s11701-021-01316-2"},{"key":"596_CR36","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.897219","volume":"9","author":"I Kil","year":"2022","unstructured":"Kil I, Eidt JF, Groff RE, Singapogu RB. Assessment of open surgery suturing skill: Simulator platform, force-based, and motion-based metrics. Front Med. 2022;9:897219.","journal-title":"Front Med"},{"key":"596_CR37","doi-asserted-by":"crossref","unstructured":"Kil I, Eidt JF, Singapogu RB, Groff RE. Assessment of open surgery suturing skill: Image-based metrics using computer vision. J Surg Educ. 2024;(accepted).","DOI":"10.1016\/j.jsurg.2024.03.020"},{"key":"596_CR38","doi-asserted-by":"crossref","unstructured":"Kil I, Groff RE, Singapogu RB. Surgical suturing with depth constraints: Image-based metrics to assess skill. In: 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2018. pp. 4146\u20139.","DOI":"10.1109\/EMBC.2018.8513266"},{"key":"596_CR39","unstructured":"Kil I. Development and preliminary validation of image-enabled process metrics for assessment of open surgery suturing skill. Clemson University; 2019.(PhD thesis)."},{"key":"596_CR40","unstructured":"Gao J. Computer vision algorithms for assessment of surgical suturing skill using hand and needle motion. Clemson University; 2024.(PhD thesis)."},{"issue":"3","key":"596_CR41","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, et al. Synchronized video and motion analysis for the assessment of procedures in the operating theater. Arch Surg. 2005;140(3):293\u20139.","journal-title":"Arch Surg"},{"key":"596_CR42","volume-title":"Essent Surg Skills","author":"DA Sherris","year":"2004","unstructured":"Sherris DA, Kern EB. Essent Surg Skills. Philadelphia, PA: Saunders; 2004."},{"key":"596_CR43","volume-title":"Practical Plastic Surgery for Nonsurgeons","author":"NB Semer","year":"2001","unstructured":"Semer NB, Adler-Lavan M. Practical Plastic Surgery for Nonsurgeons. Philadelphia, PA: Hanley & Belfus Philadelphia; 2001."},{"key":"596_CR44","doi-asserted-by":"crossref","unstructured":"Jackson RC, \u00c7avu\u015fo\u011flu MC. Needle path planning for autonomous robotic surgical suturing. In: 2013 IEEE International conference on robotics and automation. IEEE; 2013. pp. 1669\u201375.","DOI":"10.1109\/ICRA.2013.6630794"},{"key":"596_CR45","doi-asserted-by":"crossref","unstructured":"Kavathekar T, Kil I, Groff RE, Burg TC, Eidt JF, Singapogu RB. Towards quantifying surgical suturing skill with force, motion and image sensor data. In: 2017 IEEE EMBS International conference on biomedical & health informatics (BHI). 2017. pp. 169\u2013172. IEEE.","DOI":"10.1109\/BHI.2017.7897232"},{"key":"596_CR46","doi-asserted-by":"crossref","unstructured":"Jackson RC, \u00c7avu\u015fo\u011flu MC. Modeling of needle-tissue interaction forces during surgical suturing. In: 2012 IEEE international conference on robotics and automation. IEEE; 2012. p. 4675\u201380.","DOI":"10.1109\/ICRA.2012.6224756"},{"key":"596_CR47","doi-asserted-by":"crossref","unstructured":"Treil L, Neumann N, Chanes N, Lejay A, Bourcier T, Bismuth J, et al. Objective evaluation of clock face suture using the objective structured assessment of technical skill (osats) checklist. EJVES Vascular Forum. 2022;57:5\u201311.","DOI":"10.1016\/j.ejvsvf.2022.10.001"},{"key":"596_CR48","unstructured":"Ferri C, Hern\u00e1ndez-Orallo J, Flach PA. A coherent interpretation of auc as a measure of aggregated classification performance. In: proceedings of the 28th international conference on machine learning (ICML-11). 2011. pp. 657\u201364."},{"issue":"1","key":"596_CR49","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology. 1982;143(1):29\u201336.","journal-title":"Radiology"}],"container-title":["Global Surgical Education - Journal of the Association for Surgical Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44186-026-00596-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44186-026-00596-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44186-026-00596-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:00:37Z","timestamp":1791925237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44186-026-00596-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,13]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["596"],"URL":"https:\/\/doi.org\/10.1007\/s44186-026-00596-2","relation":{},"ISSN":["2731-4588"],"issn-type":[{"value":"2731-4588","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,13]]},"assertion":[{"value":"19 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Dr.\u00a0Singapogu is the founder of and holds equity in Sojourn MedTech LLC. Additionally, Sojourn MedTech LLC had no role in the study design, data collection and analysis of this study.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The experimental procedures were approved by Clemson University Institutional Review Board (IRB number: IRB2020-387).","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"196"}}