Evidence map›Paper›PMID 40167881›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

Generation of surgical reports for lymph node dissection during laparoscopic gastric cancer surgery based on artificial intelligence.

Yuhao Zhai, Zhen Chen, Xingjian Luo, Zhi Zheng, Haiqiao Zhang, Xi Wang, Xiaosheng Yan, Xiaoye Liu, Jie Yin, Jinqiao Wang and 1 more

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Yuhao Zhai *Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China.
Zhen Chen *Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Shantin, China.
Xingjian Luo *Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Shantin, China.
Zhi ZhengDepartment of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China.
Haiqiao ZhangDepartment of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China.
Xi WangDepartment of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China.
Xiaosheng YanDepartment of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China.
Xiaoye LiuDepartment of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China.
Jie YinDepartment of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China.
Jinqiao WangFoundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Haidian District, Beijing, China. jqwang@nlpr.ia.ac.cn.
Jun ZhangDepartment of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China. zhangjun5986@ccmu.edu.cn.ORCID http://orcid.org/0000-0001-5411-1273

Funding

Beijing Municipal Science &Technology Commission D17100006517003
6 · The paper itself

Abstract

purposeThis study aimed to develop an artificial intelligence (AI) model for the surgical report output of laparoscopic lymph node dissection in the suprapancreatic region during gastric cancer surgery.

methodsPatients who underwent laparoscopic radical resection for gastric cancer were included in this study, and their surgical videos were analyzed. The videos were recorded from the opening of the gastropancreatic fold as the starting point to the transection of the left gastric artery as the endpoint, with the video frame rate set to 1 frame per second. All surgical procedures were recorded following the principle of tool-tissue interaction, with annotations completed by an experienced surgeon and reviewed by a senior surgeon. The final annotated surgical videos were used as inputs for the AI model to generate the surgical report output.

resultsA total of 100 patients who underwent laparoscopic surgery for gastric cancer were included. A Surgical Concept Alignment Network was used as the model for surgical report output. The average number of frames in the videos was 728.71, with the grasping forceps being the most frequently used instrument. The AI model successfully generated a surgical video report output, achieving a BLEU-4 score of 0.7377, METEOR score of 0.4846, and ROUGE-L score of 0.7953.

conclusionThe AI model demonstrates its capability in producing surgical report output for laparoscopic lymph node dissection in the suprapancreatic region during gastric cancer surgery. This model serves as a valuable tool in clinical diagnosis, treatment, and training.

Indexed as

Artificial IntelligenceGastrectomyLaparoscopyLymph Node ExcisionStomach NeoplasmsAdultAgedFemaleHumansMaleMiddle AgedVideo RecordingArtificial intelligenceGastric cancerSuprapancreatic regionSurgical reportSurgical subtitling

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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.