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.
Danit Dayan, E. Nizri, Stephanie Rico et al.· Langenbeck's archives of sur...· 0 citations
BACKGROUND
Appendicitis severity underpins contemporary management guidelines, where laparoscopic appendectomy remains gold standard. Preoperative measures poorly predict actual disease severity or complication risk, while operative grading systems such as the American Association for the Surgery of Trauma (AAST) remains largely confined to research settings. Artificial intelligence (AI) may provide practical solutions. We evaluated a previously validated AI-derived surgical video assessment of disease severity for predicting perioperative complications.
METHODS
This retrospective study included consecutive surgical videos (6/2022-1/2024) routinely analyzed by the AI platform. AI-derived severity scores were stratified into Low (uncomplicated) and High (complicated) groups. Multivariable analysis identified independent predictors of complications. Operative-AAST served for benchmarking. Model discrimination (AUC), post hoc recalibration plot, and decision curve analysis (DCA) were evaluated.
RESULTS
Of 632 cases, 74.5% were low severity and 25.5% high. The High group had higher complication rates (26.7% vs. 10%; p<0.001), including intraoperative (11.8 vs. 3.2%; p < 0.001) and postoperative complications (15.5 vs. 7.5%; p = 0.005). AI-derived severity independently predicted complications (OR 2.76, 95% CI 1.62-4.73; p < 0.001), even after adjustment for operative-AAST (OR 1.90, 95% CI 1.07-3.36; p = 0.028). Discrimination was modest (AUC = 0.63), similar to operative-AAST (AUC = 0.68). Calibration plot showed incremental increase in complication rates across probabilities, with acceptable agreements at extremes and improved alignment at intermediate-risk. DCA showed the model had highest net benefit at intermediate-risk, comparable or higher than operative-AAST.
CONCLUSIONS
Automated AI-based surgical video assessment shows promise as a complementary tool for risk-prediction of laparoscopic appendectomy. It offers scalable risk stratification that may be implemented in routine clinical practice. Nevertheless, further study and model refinement are warranted.
Tal Kardish, M. Ortenzi, E. Nizri et al.· Surgical Endoscopy· 0 citations
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