ArticleSurgical endoscopy2025
Deep learning-based gastrocolic trunk recognition in laparoscopic right hemicolectomy.
Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntraoperative bleeding from the gastrocolic trunk is a serious complication of laparoscopic right hemicolectomy for right-sided colon cancer. Recognizing the gastrocolic trunk is crucial for surgical safety.
methodsWe developed and retrospectively evaluated a deep learning model that automatically recognizes the gastrocolic trunk during laparoscopic right hemicolectomy. Still images from laparoscopic right hemicolectomy videos were annotated at each pixel corresponding to the gastrocolic trunk and superior mesenteric vein and input into the deep learning model for segmentation of the gastrocolic trunk. Tasks were categorized as follows based on the segmentation patterns: Task A, gastrocolic trunk and superior mesenteric vein distinguished from the background; Task B, gastrocolic trunk, superior mesenteric vein, and background distinguished individually; and Task C, the gastrocolic trunk distinguished from the superior mesenteric vein and background. Data for this study were obtained from 10 high-volume hospitals in Japan. Images from 43 patients who were diagnosed with right-sided colon cancer and underwent right hemicolectomy between April 2018 and July 2020 were analyzed. Fivefold cross-validation was performed, and the average Dice coefficient, precision, and recall were evaluated.
resultsOverall, 1,625 still images from 43 laparoscopic right hemicolectomy videos were analyzed in this study. The average Dice coefficient for vein segmentations in task A was 0.84, and those for gastrocolic trunk segmentations in tasks B and C were 0.61 and 0.58, respectively.
conclusionsWe developed a deep learning model that can identify and visualize the gastrocolic trunk in surgical videos of laparoscopic right hemicolectomy, which can improve procedural safety. Future studies should confirm whether this model can effectively reduce the risk of accidental intraoperative bleeding during laparoscopic right hemicolectomy in real-world clinical settings.
Indexed as
Identifiers
40897877What OpenQuestion holds
Registered trials
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.