Evidence map›Paper›PMID 42643662›Full record

ArticleAnnals of gastroenterological surgery2026

AI-Assisted Recognition of Actual Ureteral and Pancreatic Injuries in Laparoscopic Colorectal Surgery: A Randomized Video Review Study.

Shunjin Ryu, Teppei Kamada, Kai Neki, Asuka Kuwashima, Yuta Imaizumi, Shunsuke Nakashima, Yasuhiro Takano, Yasunobu Kobayashi, Yasuhiro Takeda, Ken Eto

Abstract read
In one paragraph

Article in Annals of gastroenterological surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Shunjin RyuDepartment of Digestive Surgery Kawaguchi Municipal Medical Center Kawaguchi Saitama Japan.ORCID https://orcid.org/0000-0001-7484-049X
Teppei KamadaDepartment of Surgery The Jikei University School of Medicine Tokyo Japan.ORCID https://orcid.org/0000-0003-3119-6176
Kai NekiDepartment of Surgery Machida Municipal Hospital Tokyo Japan.
Asuka KuwashimaAnaut Inc Tokyo Japan.
Yuta ImaizumiDepartment of Digestive Surgery Kawaguchi Municipal Medical Center Kawaguchi Saitama Japan.
Shunsuke NakashimaDepartment of Digestive Surgery Kawaguchi Municipal Medical Center Kawaguchi Saitama Japan.
Yasuhiro TakanoDepartment of Surgery The Jikei University School of Medicine Tokyo Japan.
Yasunobu KobayashiDepartment of Surgery The Jikei University School of Medicine Tokyo Japan.
Yasuhiro TakedaDepartment of Surgery The Jikei University School of Medicine Tokyo Japan.
Ken EtoDepartment of Surgery The Jikei University School of Medicine Tokyo Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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%; 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.

Indexed as

artificial intelligencecolorectal surgeryEUREKAintraoperative complicationsureteral injuries

Identifiers

PMID42643662
PMCPMC13503624

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.