{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T17:38:52Z","timestamp":1792777132646,"version":"4.5.0"},"reference-count":31,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["NSERC RGPIN 2025-05278 Zheng"],"award-info":[{"award-number":["NSERC RGPIN 2025-05278 Zheng"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Surg Innov"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Counting surgical instruments during surgical procedures is critical for ensuring patient safety. Our goal is to develop a machine learning-based computer vision algorithm to create a reliable, automated surgical instrument counting support system. We hypothesize that our machine learning algorithm, based on the YOLO (You Only Look Once) model, can effectively perform instrument counting in surgery videos, providing real-time feedback to the surgical team.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>YOLO is a real-time object detection algorithm built on a convolutional neural network (CNN). To train the model, we record 100\u00a0minutes of open surgery video in simulation, including 600 instrument transfer events. Additionally, we collected 500 images of 6 different surgical instruments and obtained another 500 images from online surgeries. All images were annotated by a human expert to mark the name and locations of instruments. 80% data was allocated for model training and 20% for validation. Trained YOLO model was tested in a new surgical video for counting surgical instruments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved a satisfactory accuracy rate for instrument detection and counting. In a validation session, the model demonstrated a 72.7% success rate. In addition, the model classified large instruments more successfully than smaller instruments, such as small hemostats and needles, within the defined ROI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>YOLOv11-based computer vision model can accurately detect and count surgical instruments within a predefined region of interest in a simulated open surgical environment. Future research should focus on improving detection of small instruments, validating the system in real operating room environments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15533506261501665","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T05:53:06Z","timestamp":1792561986000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Detection and Counting of Surgical Instruments in Surgery Using AI-Based Computer Vision Model"],"prefix":"10.1177","author":[{"given":"Yile","family":"Zhu","sequence":"first","affiliation":[{"name":"University of Alberta"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Alberta"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Lai","sequence":"additional","affiliation":[{"name":"University of Alberta"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyi","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Alberta"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Alberta"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3476-5936","authenticated-orcid":false,"given":"Bin","family":"Zheng","sequence":"additional","affiliation":[{"name":"University of Alberta"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,20]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"Retained Surgical Items_ Definition and Epidemiology","author":"Gibbs MaPR V","unstructured":"Gibbs MaPR V, Md MPH. Retained Surgical Items_ Definition and Epidemiology. Agency for Healthcare Research and Quality. Accessed May 30, 2026. https:\/\/psnet.ahrq.gov\/primer\/retained-surgical-items-definition-and-epidemiology"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amsu.2016.11.006"},{"key":"e_1_3_3_4_2","first-page":"02640","article-title":"Retained surgical items: events and guidelines revisited","volume":"1506","author":"Wallace SC","year":"2017","unstructured":"Wallace SC, author. Retained surgical items: events and guidelines revisited. 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