{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T06:32:04Z","timestamp":1765175524424,"version":"3.46.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Laparoscopy is a visual biosensor that can obtain real-time images of the body cavity, assisting in minimally invasive surgery. Laparoscopic cholecystectomy is one of the most frequently performed endoscopic surgeries and the most fundamental modular surgery. However, many iatrogenic complications still occur each year, mainly due to the anatomical recognition errors of surgeons. Therefore, the development of artificial intelligence (AI)-assisted recognition is of great significance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study proposes a method based on the lightweight YOLOv11n model. By introducing the efficient multi-scale feature extraction module, DWR, the real-time performance of the model is enhanced. Additionally, the bidirectional feature pyramid network (BiFPN) is incorporated to strengthen the capability of multi-scale feature fusion. Finally, we developed the LC-YOLOmatch semi-supervised learning framework, which effectively addresses the issue of scarce labeled data in the medical field.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental results on the publicly available Cholec80 dataset show that this method achieves 70% mAP50 and 40.8% mAP50-95, reaching a new technical level and reducing the reliance on manual annotations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These improvements not only highlight its potential in automated surgeries but also significantly enhance assistance in laparoscopic procedures while effectively reducing the incidence of complications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2025.1706021","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T06:29:48Z","timestamp":1765175388000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["LC-YOLOmatch: a novel scene segmentation approach based on YOLO for laparoscopic cholecystectomy"],"prefix":"10.3389","volume":"8","author":[{"given":"Hong","family":"Long","sequence":"first","affiliation":[]},{"given":"Yuancheng","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Mini Han","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Fengshi","family":"Jing","sequence":"additional","affiliation":[]},{"given":"Yuqiao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Jia","family":"Gu","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"336","DOI":"10.3390\/computers13120336","article-title":"The YOLO framework: a comprehensive review of evolution, applications, and benchmarks in object detection","volume":"13","author":"Ali","year":"2024","journal-title":"Computers"},{"key":"B2","doi-asserted-by":"publisher","first-page":"6676","DOI":"10.1038\/s41467-023-42451-8","article-title":"Intelligent surgical workflow recognition for endoscopic submucosal dissection with real-time animal study","volume":"14","author":"Cao","year":"2023","journal-title":"Nat. Commun"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.3390\/s23041958","article-title":"Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches","volume":"23","author":"Jalal","year":"2023","journal-title":"Sensors"},{"key":"B4","unstructured":"Jocher\n              G.\n            \n            \n              Chaurasia\n              A.\n            \n            \n              Qiu\n              J.\n            \n          \n          YOLOv5 v7.0 [Computer software]\n          \n          2022"},{"key":"B5","volume-title":"Open Cholecystectomy","author":"Jones","year":"2025"},{"key":"B6","doi-asserted-by":"publisher","first-page":"4457","DOI":"10.3390\/cancers14184457","article-title":"U-Net based segmentation and characterization of gliomas","volume":"14","author":"Kihira","year":"2022","journal-title":"Cancers"},{"key":"B7","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.ijsu.2020.05.015","article-title":"Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: experimental research","volume":"79","author":"Kitaguchi","year":"2020","journal-title":"Int. J. Surg"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1315250","DOI":"10.3389\/frobt.2023.1315250","article-title":"Technologies evolution in robot-assisted fracture reduction systems: a comprehensive review","volume":"10","author":"Kou","year":"2023","journal-title":"Front. Robot. AI"},{"key":"B9","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1007\/s10439-022-03033-9","article-title":"Intraoperative detection of surgical gauze using deep convolutional neural network","volume":"51","author":"Lai","year":"2023","journal-title":"Ann. Biomed. Eng"},{"key":"B10","doi-asserted-by":"publisher","first-page":"105637","DOI":"10.1016\/j.bspc.2023.105637","article-title":"Adaptive undersampling and short clip-based two-stream CNN-LSTM model for surgical phase recognition on cholecystectomy videos","volume":"88","author":"Lee","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"B11","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1097\/SLA.0000000000004594","article-title":"Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy","volume":"276","author":"Madani","year":"2022","journal-title":"Ann. Surg"},{"key":"B12","unstructured":"Mutter\n              D.\n            \n            \n              Marescaux\n              J.\n            \n          \n          Standard Laparoscopic Cholecystectomy\n          \n          2004"},{"key":"B13","doi-asserted-by":"publisher","first-page":"198","DOI":"10.11604\/pamj.2021.38.198.27115","article-title":"Challenges in healthcare financing for surgery in sub-saharan africa","volume":"38","author":"Okoroh","year":"2021","journal-title":"Pan Afr. Med. J"},{"key":"B14","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1007\/s11548-022-02771-4","article-title":"Automated identification of critical structures in laparoscopic cholecystectomy","volume":"17","author":"Owen","year":"2022","journal-title":"Int. J. Comput. Assist. Radiol. Surg"},{"key":"B15","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1007\/s11548-024-03115-0","article-title":"DBH-YOLO: a surgical instrument detection method based on feature separation in laparoscopic surgery","volume":"19","author":"Pan","year":"2024","journal-title":"Int. J. Comput. Assist. Radiol. Surg"},{"key":"B16","doi-asserted-by":"publisher","first-page":"7376","DOI":"10.1007\/s00464-023-10323-3","article-title":"Application and evaluation of surgical tool and tool tip recognition based on convolutional neural network in multiple endoscopic surgical scenarios","volume":"37","author":"Ping","year":"2023","journal-title":"Surg. Endosc"},{"key":"B17","doi-asserted-by":"publisher","first-page":"7444","DOI":"10.1007\/s00464-022-09160-7","article-title":"Artificial intelligence software available for medical devices: surgical phase recognition in laparoscopic cholecystectomy","volume":"36","author":"Shinozuka","year":"2022","journal-title":"Surg. Endosc"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1007\/s11548-025-03408-y","article-title":"SASVi: segment any surgical video","volume":"20","author":"Sivakumar","year":"2025","journal-title":"Int. J. Comput. Assist. Radiol. Surg"},{"key":"B19","doi-asserted-by":"publisher","first-page":"e26216","DOI":"10.1016\/j.heliyon.2024.e25210","article-title":"Development of deep learning framework for anatomical landmark detection and guided dissection line during laparoscopic cholecystectomy","volume":"10","author":"Smithmaitrie","year":"2024","journal-title":"Heliyon"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2001.07685","article-title":"\u201cFixMatch: simplifying semi-supervised learning with consistency and confidence,\u201d","author":"Sohn","year":"2020","journal-title":"Advances in Neural Information Processing Systems, Vol. 33"},{"key":"B21","first-page":"10781","article-title":"\u201cEfficientdet: scalable and efficient object detection,\u201d","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Tan","year":"2020"},{"key":"B22","doi-asserted-by":"publisher","first-page":"106723","DOI":"10.1016\/j.compbiomed.2023.106723","article-title":"Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images","volume":"157","author":"Tang","year":"2023","journal-title":"Comput. Biol. Med"},{"key":"B23","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1177\/00220345231212811","article-title":"The use of dynamic navigation systems as a component of digital dentistry","volume":"103","author":"Tang","year":"2024","journal-title":"J. Dent. Res"},{"key":"B24","doi-asserted-by":"publisher","first-page":"2697","DOI":"10.1007\/s00521-024-10713-1","article-title":"Real-time object segmentation for laparoscopic cholecystectomy using YOLOv8","volume":"37","author":"Tashtoush","year":"2025","journal-title":"Neural Comput. Appl"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1007\/s00464-020-07548-x","article-title":"Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy","volume":"35","author":"Tokuyasu","year":"2021","journal-title":"Surg. Endosc"},{"key":"B26","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TMI.2016.2593957","article-title":"EndoNet: a deep architecture for recognition tasks on laparoscopic videos","volume":"36","author":"Twinanda","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B27","first-page":"2262","article-title":"\u201cWhat's it going to cost you?: predicting effort vs. informativeness for multi-label image annotations,\u201d","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Vijayanarasimhan","year":"2009"},{"key":"B28","doi-asserted-by":"publisher","first-page":"2795","DOI":"10.1109\/TMI.2020.3047807","article-title":"Annotation-efficient learning for medical image segmentation based on noisy pseudo labels and adversarial learning","volume":"40","author":"Wang","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B29","doi-asserted-by":"publisher","first-page":"4008","DOI":"10.1007\/s00464-020-07833-9","article-title":"Automated operative phase identification in peroral endoscopic myotomy","volume":"35","author":"Ward","year":"2021","journal-title":"Surg. Endosc"},{"key":"B30","first-page":"1552","article-title":"\u201cClinical text annotation-what factors are associated with the cost of time?\u201d","volume-title":"AMIA Annual Symposium Proceedings","author":"Wei","year":"2018"},{"key":"B31","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1109\/TMI.2023.3279110","article-title":"Compete to win: enhancing pseudo labels for barely-supervised medical image segmentation","volume":"42","author":"Wu","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B32","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/DDCLS61622.2024.10606792","article-title":"\u201cDetection of anatomical landmarks during laparoscopic cholecystectomy surgery based on improved YOLOv7 algorithm,\u201d","volume-title":"Proceedings of the 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)","author":"Yang","year":"2024"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1706021\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T06:29:50Z","timestamp":1765175390000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1706021\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":32,"alternative-id":["10.3389\/frai.2025.1706021"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1706021","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,8]]},"article-number":"1706021"}}