{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:06:10Z","timestamp":1790197570168,"version":"4.1.0"},"reference-count":0,"publisher":"SPIE-Intl Soc Optical Eng","issue":"09","license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61975250"],"award-info":[{"award-number":["61975250"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Biomed. Opt."],"published-print":{"date-parts":[[2025,9,16]]},"abstract":"<jats:p>SignificanceWe address the challenge of inadequate force feedback in laparoscopic surgery, which increases the risk of vessel injury. By integrating fiber Bragg grating (FBG) sensors with a laparoscopic grasper and employing a convolutional neural network combined with long short-term memory (CNN-LSTM) algorithm, this approach enables real-time, accurate vessel identification, potentially reducing surgical complications.AimLaparoscopic surgery is often hindered by inadequate force feedback, especially in complex scenarios involving tumor invasion and pelvic-abdominal adhesion, leading to challenges in locating blood vessels and an increased risk of vessel injury. Thus, it is desirable to develop a laparoscopic system capable of distinguishing the location and type of the vessels during surgery, which requires a compact and highly sensitive sensor integrated with a laparoscopic grasper.ApproachWe present an innovative laparoscopic grasper integrated with FBG force sensors for real-time force feedback, employing silicone and porcine vessel models to simulate varying depths and tissue coverage. The device successfully captured specific vessel signals, which were processed through a CNN-LSTM algorithm, enabling real-time vessel identification in minimally invasive surgery (MIS).ResultsThe intelligent laparoscopic grasper successfully obtained distinct vessel signals under varying conditions. As a result, the mean vessel gripping force for porcine vessel model III was 0.059\u00a0N under fatty tissue and 0.032\u00a0N under muscle tissue (p&lt;0.001). The CNN-LSTM algorithm achieved a precision of 97.06% in vessel identification across different tissue coverages.ConclusionsThe FBG sensor-integrated laparoscopic grasper, assisted by the processing of the CNN-LSTM algorithm, demonstrated the ability to identify vessels ex vivo across different models. This technology holds potential for real-time and accurate vessel identification during MIS, which could significantly reduce the occurrence of unnecessary vessel injuries.<\/jats:p>","DOI":"10.1117\/1.jbo.30.9.097001","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T18:53:34Z","timestamp":1758048814000},"page":"1-14","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent laparoscopic grasper with hybrid neural networks for real-time vascular detection in minimally invasive surgery"],"prefix":"10.1117","volume":"30","author":[{"given":"Pingping","family":"Wang","sequence":"first","affiliation":[{"name":"Zhujiang Hospital of the Southern Medical Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuting","family":"Huang","sequence":"additional","affiliation":[{"name":"Zhujiang Hospital of the Southern Medical Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziying","family":"Huang","sequence":"additional","affiliation":[{"name":"Southern Medical Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Huang","sequence":"additional","affiliation":[{"name":"Zhujiang Hospital of the Southern Medical Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhujiang Hospital of the Southern Medical Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengying","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhujiang Hospital of the Southern Medical Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaichao","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhujiang Hospital of the Southern Medical Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengyong","family":"Liu","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5279-9868","authenticated-orcid":false,"given":"Dongxian","family":"Peng","sequence":"additional","affiliation":[{"name":"Zhujiang Hospital of the Southern Medical Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"189","container-title":["Journal of Biomedical Optics"],"original-title":[],"link":[{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-biomedical-optics\/volume-30\/issue-09\/097001\/Intelligent-laparoscopic-grasper-with-hybrid-neural-networks-for-real-time\/10.1117\/1.JBO.30.9.097001.full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:39:33Z","timestamp":1790195973000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-biomedical-optics\/volume-30\/issue-09\/097001\/Intelligent-laparoscopic-grasper-with-hybrid-neural-networks-for-real-time\/10.1117\/1.JBO.30.9.097001.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"references-count":0,"journal-issue":{"issue":"09","published-online":{"date-parts":[[2025,9,1]]}},"URL":"https:\/\/doi.org\/10.1117\/1.jbo.30.9.097001","relation":{},"ISSN":["1083-3668","1560-2281"],"issn-type":[{"value":"1083-3668","type":"print"},{"value":"1560-2281","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]}}}