ArticleInternational journal of computer assisted radiology and surgery2020
Assisted phase and step annotation for surgical videos.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in Cataract Surgery: A Systematic Review.Translational vision science & technology · 2024Pooled it
- A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video.Surgical endoscopy · 2023Pooled it
- Action classification for endoscopic pituitary adenoma resection: a consensus-based study.International journal of computer assisted radiology and surgery · 2026Article
- Training-free detection and 6D pose estimation of unseen surgical instruments.International journal of computer assisted radiology and surgery · 2026Article
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Weakly supervised pre-training for surgical step recognition using unannotated and heterogeneously labeled videos.International journal of computer assisted radiology and surgery · 2026Article
- Article
- Artificial intelligence-assisted phase recognition and skill assessment in laparoscopic surgery: a systematic review.Frontiers in surgery · 2025Review
- A new mixed reality tool for training in minimally invasive robotic-assisted surgery.Health information science and systems · 2023Article
- Generating Operative Workflows for Vestibular Schwannoma Resection: A Two-Stage Delphi's Consensus in Collaboration with the British Skull Base Society. Part 2: The Translabyrinthine Approach.Journal of neurological surgery. Part B, Skull base · 2023Article
- Global-local multi-stage temporal convolutional network for cataract surgery phase recognition.Biomedical engineering online · 2022Article
- Surgical reporting for laparoscopic cholecystectomy based on phase annotation by a convolutional neural network (CNN) and the phenomenon of phase flickering: a proof of concept.International journal of computer assisted radiology and surgery · 2022Article
- Generic surgical process model for minimally invasive liver treatment methods.Scientific reports · 2022Article
- Cloud Based AI-Driven Video Analytics (CAVs) in Laparoscopic Surgery: A Step Closer to a Virtual Portfolio.Cureus · 2022Article
- Surgical data science - from concepts toward clinical translation.Medical image analysis · 2022Review
- Application of artificial intelligence in cataract management: current and future directions.Eye and vision (London, England) · 2022Review
- Pituitary society expert Delphi consensus: operative workflow in endoscopic transsphenoidal pituitary adenoma resection.Pituitary · 2021Article
- Artificial intelligence assisted display in thoracic surgery: development and possibilities.Journal of thoracic disease · 2021Review
- Artificial Intelligence-Assisted Surgery: Potential and Challenges.Visceral medicine · 2020Review
- Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAnnotation of surgical videos is a time-consuming task which requires specific knowledge. In this paper, we present and evaluate a deep learning-based method that includes pre-annotation of the phases and steps in surgical videos and user assistance in the annotation process.
methodsWe propose a classification function that automatically detects errors and infers temporal coherence in predictions made by a convolutional neural network. First, we trained three different architectures of neural networks to assess the method on two surgical procedures: cholecystectomy and cataract surgery. The proposed method was then implemented in an annotation software to test its ability to assist surgical video annotation. A user study was conducted to validate our approach, in which participants had to annotate the phases and the steps of a cataract surgery video. The annotation and the completion time were recorded.
resultsThe participants who used the assistance system were 7% more accurate on the step annotation and 10 min faster than the participants who used the manual system. The results of the questionnaire showed that the assistance system did not disturb the participants and did not complicate the task.
conclusionThe annotation process is a difficult and time-consuming task essential to train deep learning algorithms. In this publication, we propose a method to assist the annotation of surgical workflows which was validated through a user study. The proposed assistance system significantly improved annotation duration and accuracy.
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Registered trials
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