Usefulness of an Artificial Intelligence Model in Recognizing Recurrent Laryngeal Nerves During Robot-Assisted Minimally Invasive Esophagectomy

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.