ArticleInternational journal of computer assisted radiology and surgery2021
SD-Net: joint surgical gesture recognition and skill assessment.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Untangling surgical gesture analysis-are we even speaking the same language? a systematic review.Surgical endoscopy · 2025Pooled it
- Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review.The British journal of surgery · 2024Pooled it
- End to end AI system for surgical gesture sequence recognition and clinical outcome prediction.NPJ digital medicine · 2026Article
- Zero-shot prompt-based video encoder for surgical gesture recognition.International journal of computer assisted radiology and surgery · 2025Article
- Deep learning prediction of error and skill in robotic prostatectomy suturing.Surgical endoscopy · 2024Article
- Automatic performance evaluation of the intracorporeal suture exercise.International journal of computer assisted radiology and surgery · 2024Article
- An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery.Sensors (Basel, Switzerland) · 2023Article
- A virtual surgical prototype system based on gesture recognition for virtual surgical training in maxillofacial surgery.International journal of computer assisted radiology and surgery · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSurgical gesture recognition has been an essential task for providing intraoperative context-aware assistance and scheduling clinical resources. However, previous methods present limitations in catching long-range temporal information, and many of them require additional sensors. To address these challenges, we propose a symmetric dilated network, namely SD-Net, to jointly recognize surgical gestures and assess surgical skill levels only using RGB surgical video sequences.
methodsWe utilize symmetric 1D temporal dilated convolution layers to hierarchically capture gesture clues under different receptive fields such that features in different time span can be aggregated. In addition, a self-attention network is bridged in the middle to calculate the global frame-to-frame relativity.
resultsWe evaluate our method on a robotic suturing task from the JIGSAWS dataset. The gesture recognition task largely outperforms the state of the arts on the frame-wise accuracy up to [Formula: see text] 6 points and the F1@50 score [Formula: see text] 8 points. We also keep the 100% predicted accuracy for the skill assessment task using LOSO validation scheme.
conclusionThe results indicate that our architecture is able to obtain representative surgical video features by extensively considering the spatial, temporal and relational context from raw video input. Furthermore, the better performance in multi-task learning implies that surgical skill assessment has a complementary effects to gesture recognition task.
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