Department of Surgery, Keio University School of Medicine, Shinjuku City, Tokyo, Japan.
Department of Gastroenterological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
Ann Surg Oncol. 2024 Dec;31(13):9344-9351. doi: 10.1245/s10434-024-16157-0. Epub 2024 Sep 12.
Recurrent laryngeal nerve (RLN) palsy is a common complication in esophagectomy and its main risk factor is reportedly intraoperative procedure associated with surgeons' experience. We aimed to improve surgeons' recognition of the RLN during robot-assisted minimally invasive esophagectomy (RAMIE) by developing an artificial intelligence (AI) model.
We used 120 RAMIE videos from four institutions to develop an AI model and eight other surgical videos from another institution for AI model evaluation. AI performance was measured using the Intersection over Union (IoU). Furthermore, to verify the AI's clinical validity, we conducted the two experiments on the early identification of RLN and recognition of its location by eight trainee surgeons with or without AI.
The IoUs for AI recognition of the right and left RLNs were 0.40 ± 0.26 and 0.34 ± 0.27, respectively. The recognition of the right RLN presence in the beginning of right RLN lymph node dissection (LND) by surgeons with AI (81.3%) was significantly more accurate (p = 0.004) than that by surgeons without AI (46.9%). The IoU of right RLN during right RLN LND recognized by surgeons with AI (0.59 ± 0.18) was significantly higher (p = 0.010) than that by surgeons without AI (0.40 ± 0.29).
Surgeons' recognition of anatomical structures in RAMIE was improved by our AI system with high accuracy. Especially in right RLN LND, surgeons could recognize the RLN more quickly and accurately by using the AI model.
喉返神经（RLN）麻痹是食管切除术的常见并发症，其主要危险因素据报道与手术医生的经验相关的术中操作有关。我们旨在通过开发人工智能（AI）模型来提高手术医生在机器人辅助微创食管切除术（RAMIE）中对 RLN 的识别能力。
我们使用来自四个机构的 120 个 RAMIE 视频来开发 AI 模型，并使用来自另一个机构的另外 8 个手术视频来评估 AI 模型。使用交并比（IoU）来衡量 AI 性能。此外，为了验证 AI 的临床有效性，我们在 8 名受训外科医生中进行了两项实验，他们在有或没有 AI 的情况下，对 RLN 的早期识别及其位置的识别。
AI 识别右、左 RLN 的 IoU 分别为 0.40 ± 0.26 和 0.34 ± 0.27。有 AI 的外科医生在右 RLN 淋巴结清扫术（LND）开始时识别右 RLN 存在的准确率（81.3%）明显更高（p = 0.004），而没有 AI 的外科医生为 46.9%。有 AI 的外科医生在右 RLN LND 期间识别右 RLN 的 IoU（0.59 ± 0.18）明显更高（p = 0.010），而没有 AI 的外科医生为 0.40 ± 0.29。
我们的 AI 系统以高精度提高了外科医生对 RAMIE 中解剖结构的识别能力。特别是在右 RLN LND 中，外科医生可以通过使用 AI 模型更快、更准确地识别 RLN。
