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Automated surgical phase recognition and analysis in single-incision laparoscopic cholecystectomy using artificial

Kezhong Tang¹, Chuan Shen², Hai Hu³

¹Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, People's Republic of China. 2313055@zju.edu.cn.

Surgical Endoscopy

|July 10, 2026

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A new deep learning model, Trans-SVNet, accurately classifies and predicts surgical phases in single-incision laparoscopic cholecystectomy (SILC). This AI system can improve surgical training and patient safety by analyzing operative videos.

Area of Science:

Artificial Intelligence in Surgery
Surgical Phase Recognition
Deep Learning Models

Background:

Single-incision laparoscopic cholecystectomy (SILC) presents unique technical challenges and a steeper learning curve than conventional methods.
Developing AI systems for surgical phase recognition and prediction in SILC is crucial for enhancing training, assessing quality, and improving safety.
This study focused on creating a multicenter AI system for SILC phase recognition and prediction.

Purpose of the Study:

To develop and evaluate a multicenter deep learning-based system for surgical phase recognition and prediction specifically for SILC.
To assess the model's performance in classifying and predicting temporal transitions of surgical phases.
To investigate the impact of training data size on model performance.

Keywords:

Artificial intelligence Single-incision laparoscopic cholecystectomy Surgical phase recognition

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Main Methods:

A deep learning model (Trans-SVNet) was developed using 122 SILC videos from two medical centers.
The model was trained to identify distinct surgical phases within SILC procedures.
Performance was evaluated on 26 independent SILC videos using metrics like accuracy, precision, recall, Jaccard Index, and Mean Absolute Error (MAE) variants.

Main Results:

The Trans-SVNet model demonstrated high performance in phase classification, achieving an overall accuracy of 0.933, precision of 0.939, and recall of 0.939.
For phase transition prediction, the model achieved an overall MAE of 37s, eMAE of 34s, and pMAE of 50s.
Increasing the number of training videos significantly improved the model's performance.

Conclusions:

The Trans-SVNet model successfully enabled automatic classification and temporal prediction of surgical phases in complete SILC videos.
AI holds significant potential for analyzing large surgical datasets, leading to clinically relevant applications in the future.
Continued refinement of AI models can further enhance surgical analysis and support safer surgical practices.