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AI enhanced surgeons' faster responses to intraoperative bleeding in laparoscopic surgery: a preclinical randomized
Jing Zhang1,2, He Cai1,2, Junjie Zhang1,2
1Division of Pancreatic Surgery, Department of General Surgery, West China Hospital of Sichuan University, Chengdu, China.
Objective:
To develop and validate an artificial intelligence (AI) model for real-time detection and tracking of intraoperative bleeding sites during laparoscopic surgery, and to evaluate whether AI assistance improves surgeons' bleeding‑identification performance.
Methods:
From January to September 2023, laparoscopic surgical videos were retrospectively collected from 20 medical centers in China. Senior surgeons identified bleeding events, extracted relevant video clips, and manually annotated bleeding frames to train the AI model. Model performance was evaluated using the F1-score, mean absolute error (MAE), and mean distance error (MDE). A preclinical randomized controlled trial was subsequently conducted. Eligible surgeons were randomly assigned to the AI-assisted (experimental) group or the control group. The primary outcome was reaction time, defined as the time elapsed from the onset of bleeding to the participant's first successful click on the bleeding site.
Results:
A total of 257 laparoscopic surgical videos were collected from 20 medical centers. The training, validation, and test sets comprised 145 (56.42%), 55 (21.40%), and 57 (22.18%) videos, respectively. The model achieved an F1-score of 0.860 (95% CI, 0.838-0.878). The MAE was 3.315 (95% CI, 3.051-3.614), and the MDE was 0.087 (95% CI, 0.086-0.088). A total of 77 surgeons were randomly assigned to the experimental group (n = 38) and the control group (n = 39). Compared with the control group, the experimental group demonstrated a significantly shorter reaction time (2.53 s vs. 3.23 s, P < 0.001) and a lower negative emotion score (1.68 vs. 2.95, P < 0.001).
Conclusion:
The AI model achieves high accuracy in real‑time detection and tracking of bleeding‑site targets within laparoscopic surgical videos. Furthermore, AI‑assisted support significantly shortens surgeons' reaction time for bleeding‑site identification and reduces their negative emotional burden when confronted with bleeding events.
Trial Registration:
Chinese Clinical Trial Registry (ChiCTR2600118185).
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