AI-powered monitoring of out-of-body idle time in laparoscopic general surgery: economic and intraoperative implications
In laparoscopic surgery, uninterrupted visualization is essential for efficiency and safety. Temporary laparoscope withdrawal for image-quality restoration results in “out-of-body” (OOB) time, a historically unquantified workflow disruption. Advances in artificial intelligence (AI)-based computer vision enable large-scale OOB measurement. We aimed to quantify OOB exposure across common general-surgery procedures and estimate intraoperative and economic burden. Retrospective observational study including consecutive laparoscopic procedures performed at an urban-university hospital (October 2020-October 2025), routinely video-recorded and analyzed using an AI-platform for automated OOB-intervals detection. Primary outcomes were OOB time and percentage of operative duration. Secondary outcomes included associations with operative time, intraoperative events, and operating room costs projected using a hypothetical time-driven costing model. Analyzed procedures ( n = 5041) included appendectomy, cholecystectomy, hernia repair, colorectal resection, and metabolic-bariatric surgery. 49.6% of procedures exceeded 5% OOB exposure, 21.3% exceeded 10%, and 9.4% exceeded 15% of operative duration. OOB time was significantly associated with operative duration across procedures (Spearman ρ 0.58–0.74, all p < 0.001). In pooled adjusted models, each 5% increase in OOB was associated with an 8.3% operative duration increase; each additional 5 min corresponded to a 28.1% increase (both p < 0.001). OOB exposure significantly co-occurred alongside increased intraoperative events (OR 1.09, 95% CI 1.02–1.16, p = 0.012). Projected cumulative OOB-related modeled burden reached $758,829 ($143,529 annually), primarily concentrated in high-burden cases. AI-enabled OOB time quantification at large scale highlights its potential association with operative complexity and estimated operating-room resource utilization. Further studies are needed to clarify causal and clinical significance and validate true hospital cost implications.