{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ao X"],"funding":["the Project of the Central Government in Guidance of Local Science and Technology Development","The Key Research and Development Project of the Ministry of Science and Technology of the People's Republic of China","Joint Fund for Regional Innovation Development of the National Natural Science Foundation of China"],"pagination":["905"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12481765"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["30(1)"],"pubmed_abstract":["<h4>Background</h4>Surgical gauze is commonly used in laparoscopic procedures. However, due to its small size, limited visibility to the naked eye, and subtle tactile presence, gauze can easily be overlooked during surgery.<h4>Purpose</h4>To support surgeons in detecting gauze, reduce the risk of omission, and enhance surgical safety and efficiency, we developed a deep learning framework designed to identify gauze in laparoscopic liver surgeries.<h4>Methods</h4>In total, 33 laparoscopic liver surgery videos were collected from 2 hospitals and used as internal and external datasets. Deep learning models were trained on individual video frames to detect and segment gauze, with each frame containing one or more gauze objects. To better evaluate model performance, we introduced a quantitative "],"journal":["European journal of medical research"],"pubmed_title":["Gauze detection and segmentation in laparoscopic liver surgery: a multi-center study."],"pmcid":["PMC12481765"],"funding_grant_id":["2023YFC3605202","U22A2039","2024ZYD0270"],"pubmed_authors":["Zhou L","Li H","Wu J","Wang J","Cheng Z","QiaoLi JF","Su S","Zheng L","Leng Y","Ao X","Lin H","Gan Y","Wu T","Tang Y"],"additional_accession":[]},"is_claimable":false,"name":"Gauze detection and segmentation in laparoscopic liver surgery: a multi-center study.","description":"<h4>Background</h4>Surgical gauze is commonly used in laparoscopic procedures. However, due to its small size, limited visibility to the naked eye, and subtle tactile presence, gauze can easily be overlooked during surgery.<h4>Purpose</h4>To support surgeons in detecting gauze, reduce the risk of omission, and enhance surgical safety and efficiency, we developed a deep learning framework designed to identify gauze in laparoscopic liver surgeries.<h4>Methods</h4>In total, 33 laparoscopic liver surgery videos were collected from 2 hospitals and used as internal and external datasets. Deep learning models were trained on individual video frames to detect and segment gauze, with each frame containing one or more gauze objects. To better evaluate model performance, we introduced a quantitative ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-03T23:53:04.021Z","creation":"2026-05-03T03:11:30.275Z"},"accession":"S-EPMC12481765","cross_references":{"pubmed":["41024181"],"doi":["10.1186/s40001-025-03190-2"]}}