Related Experiment Video
Updated: Aug 27, 2026
Laparoscopic Duodenum-Preserving Pancreatic Head Resection via Inferior Infracolic Approach: A Surgical Approach for Benign Lesions
Published on: February 9, 2024
AI-Assisted Recognition of Actual Ureteral and Pancreatic Injuries in Laparoscopic Colorectal Surgery: A Randomized
Shunjin Ryu1,2, Teppei Kamada2, Kai Neki3
1Department of Digestive Surgery Kawaguchi Municipal Medical Center Kawaguchi Saitama Japan.
Aim:
Artificial Intelligence (AI)-assisted anatomical recognition may help surgeons recognize intraoperative adverse events missed during surgery. We evaluated whether AI-enhanced videos improved recognition of ureteral and pancreatic injuries.
Methods:
In this two-institution randomized video-review study using historical laparoscopic colorectal surgery videos, 150 surgeons reviewed either EUREKA-generated AI-enhanced videos or unenhanced videos. Five videos were reviewed: three actual injury videos, including unrecognized injuries, and two clinically comparable no-injury control videos. The primary outcome was correct injured-organ recognition. Secondary outcomes included recognition within the predefined injury time-code window and correct no-adverse-event judgment.
Results:
Seventy-five surgeons were assigned to each group, with no significant baseline differences. AI-enhanced videos improved ureteral injury recognition in Video A (40.0% vs. 22.7%; p = 0.022) and Video C (50.7% vs. 28.0%; p < 0.01). Time-code window recognition was greater for Video A (20.0% vs. 5.3%; p < 0.01) and Video C (18.7% vs. 4.0%; p < 0.01). Pancreatic injury recognition in Video D did not differ overall (77.3% vs. 70.7%; p = 0.352) but was greater among surgeons without advanced laparoscopic certification (81.3% vs. 63.3%; p = 0.048). Correct no-adverse-event judgments did not differ between groups.
Conclusion:
AI-generated anatomical highlighting improved surgeons' recognition of actual ureteral injuries under controlled video-review conditions without increasing incorrect adverse-event judgments. These findings provide proof-of-concept evidence that AI-generated overlays may support timely recognition of clinically important intraoperative adverse events. Prospective intraoperative studies are needed to determine whether this recognition benefit facilitates earlier management, changes operative decision-making, reduces complications, or improves patient outcomes.
Trial Registration:
UMIN-CTR; UMIN ID: UMIN000059439.
