人工智能辅助腹腔镜胆囊切除术在猪临床前模型中的应用

Khalid Mohammed Ali, Michele Saruwatari, Kochai Jawed, Yoseph Kim, Seihoon Park, Seongjun Cha, Bo Ning, R. J. Cha
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引用次数: 0

摘要

腹腔镜胆囊切除术中医源性胆管损伤发生率为1-2%。导管的小尺寸,多种解剖变异和炎症条件使得外科医生在解剖过程中识别或保护导管具有挑战性。尽管在手术中识别胆管存在困难和日益增加的担忧,但除了术中胆管造影外,外科医生还没有一种特定的方式来识别胆管,这扰乱了工作流程。在这里,我们介绍了我们在猪体内模型中使用准实时人工智能辅助胆道特异性荧光成像的腹腔镜胆囊切除术的新研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence-Assisted Laparoscopic Cholecystectomy in a Preclinical Swine Model
Iatrogenic bile duct injury during laparoscopic cholecystectomy has an incidence rate of 1-2%. The small size of the duct, multiple anatomical variations, and inflammatory conditions make it challenging for the surgeon to identify or protect it during dissection. Despite the difficulties and rising concerns about identifying the bile duct during surgeries, surgeons do not have a specific modality to identify the bile duct except intraoperative cholangiography, which disrupts workflows. Here, we present our new study on laparoscopic cholecystectomy using a quasi-real-time artificial intelligence-assisted biliary-specific fluorescence imaging in a swine model in vivo.
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