ArticleInternational journal of computer assisted radiology and surgery2021
Surgical workflow recognition with 3DCNN for Sleeve Gastrectomy.
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 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video.Surgical endoscopy · 2023Pooled it
- In-depth characterization of a laparoscopic radical prostatectomy procedure based on surgical process modeling.International journal of computer assisted radiology and surgery · 2026Article
- Systematic analysis of evaluation metrics for deep learning-based surgical phase recognition in laparoscopic cholecystectomy.Frontiers in artificial intelligence · 2026Review
- Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review.BJS open · 2025Article
- Artificial intelligence-assisted phase recognition and skill assessment in laparoscopic surgery: a systematic review.Frontiers in surgery · 2025Review
- Enhancing Human Activity Recognition through Integrated Multimodal Analysis: A Focus on RGB Imaging, Skeletal Tracking, and Pose Estimation.Sensors (Basel, Switzerland) · 2024Article
- Surgical phase and instrument recognition: how to identify appropriate dataset splits.International journal of computer assisted radiology and surgery · 2024Article
- Artificial intelligence for automatic surgical phase recognition of laparoscopic gastrectomy in gastric cancer.International journal of computer assisted radiology and surgery · 2024Article
- Current and future applications of artificial intelligence in surgery: implications for clinical practice and research.Frontiers in surgery · 2024Review
- Intelligent surgical workflow recognition for endoscopic submucosal dissection with real-time animal study.Nature communications · 2023Article
- An artificial intelligence model that automatically labels roux-en-Y gastric bypasses, a comparison to trained surgeon annotators.Surgical endoscopy · 2023Article
- Surgical Phase Recognition in Inguinal Hernia Repair-AI-Based Confirmatory Baseline and Exploration of Competitive Models.Bioengineering (Basel, Switzerland) · 2023Article
- Surgical workflow recognition with temporal convolution and transformer for action segmentation.International journal of computer assisted radiology and surgery · 2023Article
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Authors and funding
5 authors.
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Abstract
purposeSurgical workflow recognition is a crucial and challenging problem when building a computer-assisted surgery system. Current techniques focus on utilizing a convolutional neural network and a recurrent neural network (CNN-RNN) to solve the surgical workflow recognition problem. In this paper, we attempt to use a deep 3DCNN to solve this problem.
methodsIn order to tackle the surgical workflow recognition problem and the imbalanced data problem, we implement a 3DCNN workflow referred to as I3D-FL-PKF. We utilize focal loss (FL) to train a 3DCNN architecture known as Inflated 3D ConvNet (I3D) for surgical workflow recognition. We use prior knowledge filtering (PKF) to filter the recognition results.
resultsWe evaluate our proposed workflow on a large sleeve gastrectomy surgical video dataset. We show that focal loss can help to address the imbalanced data problem. We show that our PKF can be used to generate smoothed prediction results and improve the overall accuracy. We show that the proposed workflow achieves 84.16% frame-level accuracy and reaches a weighted Jaccard score of 0.7327 which outperforms traditional CNN-RNN design.
conclusionThe proposed workflow can obtain consistent and smooth predictions not only within the surgical phases but also for phase transitions. By utilizing focal loss and prior knowledge filtering, our implementation of deep 3DCNN has great potential to solve surgical workflow recognition problems for clinical practice.
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