Evaluation of surgical complexity by automated surgical process recognition in robotic distal gastrectomy using artificial intelligence

Abstract

Background: Although radical gastrectomy with lymph node dissection is the standard treatment for gastric cancer, the complication rate remains high. Thus, estimation of surgical complexity is required for safety. We aim to investigate the association between the surgical process and complexity, such as a risk of complications in robotic distal gastrectomy (RDG), to establish an artificial intelligence (AI)-based automated surgical phase recognition by analyzing robotic surgical videos, and to investigate the predictability of surgical complexity by AI.

Method: This study assessed clinical data and robotic surgical videos for 56 patients who underwent RDG for gastric cancer. We investigated (1) the relationship between surgical complexity and perioperative factors (patient characteristics, surgical process); (2) AI training for automated phase recognition and model performance was assessed by comparing predictions to the surgeon-annotated reference; (3) AI model predictability for surgical complexity was calculated by the area under the curve.

Result: Surgical complexity score comprised extended total surgical duration, bleeding, and complications and was strongly associated with the intraoperative surgical process, especially in the beginning phases (area under the curve 0.913). We established an AI model that can recognize surgical phases from video with 87% accuracy; AI can determine intraoperative surgical complexity by calculating the duration of beginning phases from phases 1-3 (area under the curve 0.859).

Conclusion: Surgical complexity, as a surrogate of short-term outcomes, can be predicted by the surgical process, especially in the extended duration of beginning phases. Surgical complexity can also be evaluated with automation using our artificial intelligence-based model.

Fig. 1

Representative image for each of the ten surgical phases (phases 1–10); (1) preparation, (2) left gastroepiploic LND, (3) infrapyloric LND, (4) supraduodenal LND, (5) duodenal resection, (6) suprapancreatic LND, (7) lesser curvature LND (8) gastric resection, (9-a) Roux-en-Y reconstruction, (9-b) Billroth-I reconstruction, and (10) after dissection to completion of surgery

Fig. 2

Example of timeline visualization of the phases from the videos for TeCNO prediction. Upper row, ground truth; lower row, TeCNO predictions

Fig. 3

Receiver operating characteristic curve analysis for the beginning of surgical process predicted by artificial intelligence