{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T08:08:48Z","timestamp":1779178128567,"version":"3.51.4"},"reference-count":66,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Surg Innov"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) enables hand motion tracking from standard surgical video recordings; however, translating these data into meaningful performance metrics remains challenging. We evaluated the preliminary validity of a markerless, AI-driven system that generates interpretable technical skill scores from an open-surgery task.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Sixteen medical students and one instructor performed a one-handed knot-tying task recorded with a smartphone camera while wearing motion sensors beneath surgical gloves. A deep learning algorithm tracking 21 hand joints mapped wrist trajectories and generated visualization boundaries from which kinematic parameters were derived and grouped into three domains scored from 0 to 10: economy of motion (EM), flow of motion (FM), and spatial organization (SO). AI metrics were validated against sensor-based data. Parameters and domain scores were correlated with expert-rated product quality (PQ) and technical performance (TP) using validated checklists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Nineteen performances were analyzed. AI metrics demonstrated strong correlations with sensor-based measures (r = 0.79-0.88,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.01). EM metrics (path length, number of movements, task time) were associated with PQ and TP (r = 0.59-0.67,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.01). Smoothness within the FM domain correlated with PQ and TP (r = 0.56-0.57,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.01), while the composite FM score moderately correlated with TP (r = 0.44,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.057). Working area within the SO domain demonstrated a moderate association with TP (r = 0.41,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.08).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This prototype AI framework translated hand kinematics into interpretable, cohort-normalized domain-level scores that aligned with expert assessment. The findings support the feasibility of video-based kinematic scoring and provide preliminary evidence of construct validity. Further studies are warranted to determine reliability and generalizability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15533506261451990","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T13:07:20Z","timestamp":1778764040000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Based Markerless Computer Vision Framework for Open Surgery Skill Assessment: A Prototype Assessment Framework"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3663-6882","authenticated-orcid":false,"given":"Alejandro","family":"Zulbaran-Rojas","sequence":"first","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Dehghan","family":"Rouzi","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"},{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natasha","family":"Hansraj","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"},{"name":"Baylor College of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Derek","family":"Erstad","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4419-1761","authenticated-orcid":false,"given":"Miguel","family":"Bargas-Ochoa","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ethan","family":"D\u2019Silva","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Randall Parker","family":"Kirby","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nilson","family":"Salas","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"},{"name":"Baylor College of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yesenia","family":"Rojas","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"},{"name":"Baylor College of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0320-8101","authenticated-orcid":false,"given":"Bijan","family":"Najafi","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"},{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamcollsurg.2007.01.016"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2023.07.013"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/bjs.1800840237"},{"key":"e_1_3_5_5_2","first-page":"e44654","article-title":"Exploring the role of simulation training in improving surgical skills among residents: a narrative review","volume":"15","author":"Cardoso SA","year":"2023","unstructured":"Cardoso SA, Suyambu J, Iqbal J, et al. Exploring the role of simulation training in improving surgical skills among residents: a narrative review. Cureus. 2023;15:e44654.","journal-title":"Cureus"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00595-023-02726-5"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00596-8"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmedt.2022.1076755"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00566-0"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsurg.2019.06.011"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamasurg.2023.6262"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-022-09509-y"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-022-09781-y"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-84295-6"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/s23094496"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apergo.2020.103136"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.3171\/2023.3.FOCUS2380"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1097\/SIH.0000000000000544"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000002478"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.surg.2016.05.004"},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.surg.2014.04.054"},{"key":"e_1_3_5_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjsurg.2005.11.014"},{"key":"e_1_3_5_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsurg.2020.05.004"},{"key":"e_1_3_5_24_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22197328"},{"key":"e_1_3_5_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2021.01.006"},{"key":"e_1_3_5_26_2","volume-title":"MediaPipe Hands: On-Device real-time Hand Tracking","author":"Zhang F","year":"2020","unstructured":"Zhang F, Bazarevsky V, Vakunov A, et al. MediaPipe Hands: On-Device real-time Hand Tracking. 2020."},{"key":"e_1_3_5_27_2","doi-asserted-by":"crossref","unstructured":"Wei SE Ramakrishna V Kanade T et al. Convolutional Pose Machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:4724-4732.","DOI":"10.1109\/CVPR.2016.511"},{"key":"e_1_3_5_28_2","doi-asserted-by":"crossref","unstructured":"Cao Z Simon T Wei SE et al. Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017:1302-1310.","DOI":"10.1109\/CVPR.2017.143"},{"issue":"3","key":"e_1_3_5_29_2","first-page":"692","article-title":"An attempt to gender determine with phalanx length and the ratio of phalanxes to whole phalanx length in direct hand radiography","volume":"8","author":"\u00d6zsoy T","year":"2019","unstructured":"\u00d6zsoy T, \u00d6ner Z, Oner S. An attempt to gender determine with phalanx length and the ratio of phalanxes to whole phalanx length in direct hand radiography. Medicine Science International Medical Journal 2019;8(3):692-697.","journal-title":"Medicine Science International Medical Journal"},{"key":"e_1_3_5_30_2","doi-asserted-by":"publisher","DOI":"10.3171\/2024.1.SPINE231001"},{"key":"e_1_3_5_31_2","doi-asserted-by":"publisher","DOI":"10.1002\/9780470549148"},{"key":"e_1_3_5_32_2","doi-asserted-by":"publisher","DOI":"10.1080\/08941939.2017.1387624"},{"key":"e_1_3_5_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjsurg.2014.12.013"},{"key":"e_1_3_5_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjsurg.2015.10.005"},{"key":"e_1_3_5_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-005-3011-3"},{"key":"e_1_3_5_36_2","doi-asserted-by":"publisher","DOI":"10.1177\/1055665618793768"},{"key":"e_1_3_5_37_2","first-page":"2126","article-title":"A robust and sensitive metric for quantifying movement smoothness","volume":"59","author":"Balasubramanian S","year":"2012","unstructured":"Balasubramanian S, Melendez-Calderon A, Burdet E. A robust and sensitive metric for quantifying movement smoothness. IEEE (Inst Electr Electron Eng) Trans Biomed Eng. 2012;59:2126-2136.","journal-title":"IEEE (Inst Electr Electron Eng) Trans Biomed Eng"},{"key":"e_1_3_5_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s004649900934"},{"key":"e_1_3_5_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2019.09.006"},{"key":"e_1_3_5_40_2","doi-asserted-by":"publisher","DOI":"10.3390\/s23063146"},{"key":"e_1_3_5_41_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12909-021-02590-2"},{"key":"e_1_3_5_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsurg.2015.01.002"},{"key":"e_1_3_5_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2017.07.081"},{"key":"e_1_3_5_44_2","doi-asserted-by":"publisher","DOI":"10.1213\/ANE.0000000000002864"},{"key":"e_1_3_5_45_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamasurg.2020.3007"},{"key":"e_1_3_5_46_2","doi-asserted-by":"publisher","DOI":"10.1097\/01.sla.0000080829.29028.c4"},{"key":"e_1_3_5_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2023.12.053"},{"key":"e_1_3_5_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2013.12.009"},{"key":"e_1_3_5_49_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12984-015-0090-9"},{"key":"e_1_3_5_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2016.2545247"},{"key":"e_1_3_5_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsurg.2016.10.006"},{"key":"e_1_3_5_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00268-020-05916-1"},{"key":"e_1_3_5_53_2","doi-asserted-by":"publisher","DOI":"10.1021\/acsbiomaterials.9b01019"},{"key":"e_1_3_5_54_2","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2021.625003"},{"key":"e_1_3_5_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2020.02.047"},{"key":"e_1_3_5_56_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2015.09.026"},{"key":"e_1_3_5_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2019.2919744"},{"key":"e_1_3_5_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-023-09905-y"},{"key":"e_1_3_5_59_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.34658"},{"key":"e_1_3_5_60_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11605-007-0357-y"},{"key":"e_1_3_5_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.urology.2012.10.020"},{"key":"e_1_3_5_62_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2016.06.038"},{"key":"e_1_3_5_63_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13019-024-02558-5"},{"key":"e_1_3_5_64_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12894-015-0051-4"},{"key":"e_1_3_5_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24553-9_53"},{"key":"e_1_3_5_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2024.09.069"},{"key":"e_1_3_5_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2020.3035763"}],"container-title":["Surgical Innovation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15533506261451990","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15533506261451990","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15533506261451990","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:46:55Z","timestamp":1779176815000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15533506261451990"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,14]]},"references-count":66,"alternative-id":["10.1177\/15533506261451990"],"URL":"https:\/\/doi.org\/10.1177\/15533506261451990","relation":{},"ISSN":["1553-3506","1553-3514"],"issn-type":[{"value":"1553-3506","type":"print"},{"value":"1553-3514","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,14]]},"article-number":"15533506261451990"}}