Evidence map›Paper›PMID 40316752›Full record

ArticleSurgical endoscopy2025

Automatic gesture recognition and evaluation in peg transfer tasks of laparoscopic surgery training.

Shujun Ju, Penglin Jiang, Yutong Jin, Yaoyu Fu, Xiandi Wang, Xiaomei Tan, Ying Han, Rong Yin, Dan Pu, Kang Li

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In one paragraph

Article in Surgical endoscopy, 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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Shujun Ju *West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Penglin Jiang *Department of Industrial Engineering, Sichuan University, Chengdu, China.
Yutong JinDepartment of Industrial Engineering, Sichuan University, Chengdu, China.
Yaoyu FuWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Xiandi WangWest China Medical Simulation Center, West China Hospital, Chengdu, China.
Xiaomei TanDepartment of Industrial Engineering, Sichuan University, Chengdu, China.
Ying HanWest China Medical Simulation Center, West China Hospital, Chengdu, China. hy-801606@wchscu.cn.
Rong YinWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China. rong.yin@scupi.cn.ORCID http://orcid.org/0000-0003-4596-5407
Dan PuWest China Medical Simulation Center, West China Hospital, Chengdu, China.
Kang LiWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.

Funding

West China Hospital, Sichuan University ZYYC21004
6 · The paper itself

Abstract

backgroundLaparoscopic surgery training is gaining increasing importance. To release doctors from the burden of manually annotating videos, we proposed an automatic surgical gesture recognition model based on the Fundamentals of Laparoscopic Surgery (FLS) and the Chinese Laparoscopic Skills Testing and Assessment (CLSTA) tools. Furthermore, statistical analysis was conducted based on a gesture vocabulary that had been designed to examine differences between groups at different levels.

methodsBased on the CLSTA, the training process of peg transfer can be represented by a standard sequence of seven surgical gestures defined in our gesture vocabulary. The dataset used for model training and testing included eighty videos recorded at 30 fps. All videos were rated by senior medical professionals from our medical training center. The dataset was processed using cross-validation to ensure robust model performance. The model applied is 3D ResNet-18, a convolutional neural network (CNN). An LSTM neural network was utilized to refine the output sequence.

resultsThe overall accuracy for the recognition model was 83.8% and the F1 score was 84%. The LSTM network improved model performance to 85.84% accuracy and an 85% F1 score. Every operative process starts with Gesture 1 (G1) and ends with G5, with wrong placement is labeled as G6. The average training time is 92 s (SD = 36). Variance was observed between groups for G1, G3, and G6, indicating that trainees may benefit from focusing their efforts on these relevant operations, while assisting doctors also in more effectively analyzing the training outcome.

conclusionAn automatic surgical gesture recognition model was developed for the peg transfer task. We also defined a gesture vocabulary along with the artificial intelligence model to sequentially describe the training operation. This provides an opportunity for artificial intelligence-enabled objective and automatic evaluation based on CLSTA in the clinic implementation.

Indexed as

Clinical CompetenceGesturesLaparoscopyHumansNeural Networks, ComputerVideo RecordingArtificial intelligenceConvolutional neural networkLaparoscopic surgery trainingMachine learningSurgical action recognition

Identifiers

PMID40316752

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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.