Automated Assessment of Surgical Quality in Distal Gastrectomy: Development of a Novel Computer Vision Model Based on the Critical View of Quality (CVQ).

Abstract

To evaluate the clinical validity of the critical view of quality (CVQ) as a measure of lymphadenectomy quality in minimally invasive distal gastrectomy and to develop a computer vision model for automated CVQ assessment. Objective intraoperative assessment of lymphadenectomy quality in gastric cancer surgery remains limited, relying largely on postoperative surrogate markers such as lymph node yield. This retrospective study included 260 patients who underwent laparoscopic or robotic distal gastrectomy with complete intraoperative video recordings. CVQ was defined as an anatomy-based scoring system reflecting the completeness of lymph node dissection across five stations. Associations between CVQ and lymph node yield were evaluated using correlation and multivariable linear regression analyses. A computer vision model was developed to classify CVQ components as complete or incomplete using temporally contextualized video segments. CVQ demonstrated a moderate positive correlation with lymph node yield (Pearson r=0.485, P<0.001; Spearman ρ=0.484, P<0.001) and remained independently associated after adjustment for clinical covariates (β=4.79, 95% CI 3.83-5.74, P<0.001). Lymph node retrieval increased across CVQ quartiles (28.9 vs. 47.6 nodes, P<0.001). Lower CVQ scores were associated with older age, higher BMI, higher ASA classification, male sex, and surgeon-related variability (all P<0.05). CVQ was not associated with short-term postoperative outcomes. Automated CVQ classification achieved average precision up to 91.5%. CVQ is a clinically meaningful measure of lymphadenectomy quality that correlates with lymph node yield while capturing operative difficulty and surgeon-related variability. Automated assessment using surgical video is feasible and may enable scalable evaluation of intraoperative performance.