Detection of blood stains using computer vision-based algorithms and their association with postoperative outcomes in thoracoscopic lobectomies

OBJECTIVES: Our goal was to develop high throughput computer vision (CV) algorithms to detect blood stains in thoracoscopic surgery and to determine how the detected blood stains are associated with postoperative outcomes. METHODS: Blood pixels in surgical videos were identified by CV algorithms trained with thousands of blood and non-blood pixels randomly selected and manually labelled. The proportion of blood pixels (PBP) was computed for key video frames to summarize the blood stain information during surgery. Statistical regression analyses were utilized to investigate the potential association between PBP and postoperative outcomes, including drainage volume, prolonged tube indwelling duration (≥5 days) and bleeding volume. RESULTS: A total of 275 patients undergoing thoracoscopic lobectomy were enrolled. The sum of PBP after flushing (P < 0.022), age (P = 0.005), immediate postoperative air leakage (P < 0.001), surgical duration (P = 0.001) and intraoperative bleeding volume (P = 0.033) were significantly associated with drainage volume in multivariable linear regression analysis. After adjustment using binary logistic regression analysis, the sum of the PBP after flushing [P = 0.017, odds ratio 1.003, 95% confidence interval (CI) 1.000–1.005] and immediate postoperative air leakage (P < 0.001, odds ratio 4.616, 95% CI 1.964–10.847) were independent predictors of prolonged tube indwelling duration. In the multivariable linear regression analysis, surgical duration (P < 0.001) and the sum of the PBP of the surgery (P = 0.005) were significantly correlated with intraoperative bleeding volume. CONCLUSIONS: This is the first study on the correlation between CV and postoperative outcomes in thoracoscopic surgery. CV algorithms can effectively detect from surgical videos information that has good prediction power for postoperative outcomes.