Department of Gynecologic Surgery, University Hospitals of Strasbourg, Avenue Molière, 67200, Strasbourg, France.
Insitute of Image-Guided Surgery, IHU-Strasbourg (Institut Hospitalo-Universitaire), Strasbourg, France.
Surg Endosc. 2022 Dec;36(12):9224-9233. doi: 10.1007/s00464-022-09409-1. Epub 2022 Jul 13.
To prove feasibility of multimodal and temporal fusion of laparoscopic images with preoperative computed tomography scans for a real-time in vivo-targeted lymph node (TLN) detection during minimally invasive pelvic lymphadenectomy and to validate and enable such guidance for safe and accurate sentinel lymph node dissection, including anatomical landmarks in an experimental model.
A measurement campaign determined the most accurate tracking system (UR5-Cobot versus NDI Polaris). The subsequent interventions on two pigs consisted of an identification of artificial TLN and anatomical landmarks without and with augmented reality (AR) assistance. The AR overlay on target structures was quantitatively evaluated. The clinical relevance of our system was assessed via a questionnaire completed by experienced and trainee surgeons.
An AR-based robotic assistance system that performed real-time multimodal and temporal fusion of laparoscopic images with preoperative medical images was developed and tested. It enabled the detection of TLN and their surrounding anatomical structures during pelvic lymphadenectomy. Accuracy of the CT overlay was > 90%, with overflow rates < 6%. When comparing AR to direct vision, we found that scores were significatively higher in AR for all target structures. AR aided both experienced surgeons and trainees, whether it was for TLN, ureter, or vessel identification.
This computer-assisted system was reliable, safe, and accurate, and the present achievements represent a first step toward a clinical study.
为了证明在微创骨盆淋巴结清扫术中对术前计算机断层扫描进行腹腔镜图像的多模态和时相融合的可行性，实现实时活体靶向淋巴结（TLN）检测，并验证和支持这种基于解剖标志的安全、准确的前哨淋巴结解剖，我们在实验模型中进行了研究。
一项测量研究确定了最准确的跟踪系统（UR5-Cobot 与 NDI Polaris）。随后，对两只猪进行干预，分别在没有和有增强现实（AR）辅助的情况下识别人工 TLN 和解剖标志。对目标结构的 AR 叠加进行了定量评估。经验丰富的外科医生和受训医生完成了问卷调查，评估了我们系统的临床相关性。
我们开发并测试了一种基于 AR 的机器人辅助系统，该系统能够实时对腹腔镜图像和术前医学图像进行多模态和时相融合。它可以在骨盆淋巴结清扫术中检测 TLN 及其周围的解剖结构。CT 叠加的准确率超过 90%，溢出率小于 6%。当将 AR 与直接视觉进行比较时，我们发现所有目标结构的 AR 评分都明显更高。AR 有助于经验丰富的外科医生和受训医生识别 TLN、输尿管或血管。
该计算机辅助系统可靠、安全、准确，目前的研究成果是朝着临床研究迈出的第一步。
