用AI实时识别肝内血管，提升腹腔镜肝切除手术精度
Towards Real-time Intrahepatic Vessel Identification in Intraoperative Ultrasound-Guided Liver Surgery
- 基于术前3D超声构建个性化深度学习模型
- 在猪肝样本上实现0.95精确率与0.93召回率
- 适合追求术中精准导航的肝外科医生
腹腔镜肝切除术相比传统开腹手术并发症更少且患者预后相当，但因难以呈现肝脏内部结构而限制普及。腹腔镜术中超声提供高效、低成本且无辐射的引导方式。本文旨在通过该技术辅助医生识别肝脏内部结构。提出一种基于术前3D超声肝体积的个性化方法，训练深度学习模型实现实时识别门静脉树及其分支。该个性化AI模型在离体猪肝验证中表现优于外科医生，精确率达0.95，召回率达0.93，为基于超声的肝切除术中血管精准识别奠定基础。其可适配性与潜在临床价值有望推动外科干预进步并改善患者治疗效果。
原文摘要 · Abstract (English)
While laparoscopic liver resection is less prone to complications and maintains patient outcomes compared to traditional open surgery, its complexity hinders widespread adoption due to challenges in representing the liver's internal structure. Laparoscopic intraoperative ultrasound offers efficient, cost-effective and radiation-free guidance. Our objective is to aid physicians in identifying internal liver structures using laparoscopic intraoperative ultrasound. We propose a patient-specific approach using preoperative 3D ultrasound liver volume to train a deep learning model for real-time identification of portal tree and branch structures. Our personalized AI model, validated on ex vivo swine livers, achieved superior precision (0.95) and recall (0.93) compared to surgeons, laying groundwork for precise vessel identification in ultrasound-based liver resection. Its adaptability and potential clinical impact promise to advance surgical interventions and improve patient care.
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