Background: Although a number of robot-assisted minimally invasive esophagectomy (RAMIE) procedures have been performed due to three-dimensional field of view, image stabilization, and flexible joint function, both the surgeons and surgical teams require proficiency. This study aimed to establish an artificial intelligence (AI)-based automated surgical-phase recognition system for RAMIE by analyzing robotic surgical videos. Methods: This study enrolled 31 patients who underwent RAMIE. The videos were annotated into the following nine surgical phases: preparation, lower mediastinal dissection, upper mediastinal dissection, azygos vein division, subcarinal lymph node dissection (LND), right recurrent laryngeal nerve (RLN) LND, left RLN LND, esophageal transection, and post-dissection to completion of surgery to train the AI for automated phase recognition. An additional phase (“no step”) was used to indicate video sequences upon removal of the camera from the thoracic cavity. All the patients were divided into two groups, namely, early period (20 patients) and late period (11 patients), after which the relationship between the surgical-phase duration and the surgical periods was assessed. Results: Fourfold cross validation was applied to evaluate the performance of the current model. The AI had an accuracy of 84%. The preparation (p = 0.012), post-dissection to completion of surgery (p = 0.003), and “no step” (p < 0.001) phases predicted by the AI were significantly shorter in the late period than in the early period. Conclusions: A highly accurate automated surgical-phase recognition system for RAMIE was established using deep learning. Specific phase durations were significantly associated with the surgical period at the authors’ institution.
ASJC Scopus subject areas