ArticleSurgical endoscopy2019
Surgical phase modelling in minimal invasive surgery.
Article in Surgical endoscopy, 2019. 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.
- A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video.Surgical endoscopy · 2023Pooled it
- Review of automated performance metrics to assess surgical technical skills in robot-assisted laparoscopy.Surgical endoscopy · 2022Pooled it
- Steerable DROP-IN radioguidance during minimal-invasive non-robotic cervical and endometrial sentinel lymph node surgery.European journal of nuclear medicine and molecular imaging · 2024Trial
- AI-based assessment of surgical efficiency and exposure quality in laparoscopic distal gastrectomy using a phase recognition model.Surgical endoscopy · 2026Article
- In-depth characterization of a laparoscopic radical prostatectomy procedure based on surgical process modeling.International journal of computer assisted radiology and surgery · 2026Article
- International expert consensus-driven surgical process model for robot-assisted hysterectomy: Delphi study results.Surgical endoscopy · 2026Article
- Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review.BJS open · 2025Article
- Automatic gesture recognition and evaluation in peg transfer tasks of laparoscopic surgery training.Surgical endoscopy · 2025Article
- Artificial intelligence for intraoperative video analysis in robotic-assisted esophagectomy.Surgical endoscopy · 2025Review
- Automated surgical skill assessment in colorectal surgery using a deep learning-based surgical phase recognition model.Surgical endoscopy · 2024Article
- Artificial intelligence for automatic surgical phase recognition of laparoscopic gastrectomy in gastric cancer.International journal of computer assisted radiology and surgery · 2024Article
- Artificial Intelligence-What to Expect From Machine Learning and Deep Learning in Hernia Surgery.Journal of abdominal wall surgery : JAWS · 2024Review
- Process model analysis of parenchyma sparing laparoscopic liver surgery to recognize surgical steps and predict impact of new technologies.Surgical endoscopy · 2023Article
- Artificial intelligence-based automated laparoscopic cholecystectomy surgical phase recognition and analysis.Surgical endoscopy · 2022Observational
- Quantitative Comparison of Surgical Device Usage in Laparoscopic Gastrectomy Between Surgeons' Skill Levels: an Automated Analysis Using a Neural Network.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2022Article
- State-of-the-art of situation recognition systems for intraoperative procedures.Medical & biological engineering & computing · 2022Review
- Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision.Surgical endoscopy · 2022Article
- Deep learning for surgical phase recognition using endoscopic videos.Surgical endoscopy · 2021Article
- Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach.Surgical endoscopy · 2020Observational
- Assisted phase and step annotation for surgical videos.International journal of computer assisted radiology and surgery · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurgical Process Modelling (SPM) offers the possibility to automatically gain insight in the surgical workflow, with the potential to improve OR logistics and surgical care. Most studies have focussed on phase recognition modelling of the laparoscopic cholecystectomy, because of its standard and frequent execution. To demonstrate the broad applicability of SPM, more diverse and complex procedures need to be studied. The aim of this study is to investigate the accuracy in which we can recognise and extract surgical phases in laparoscopic hysterectomies (LHs) with inherent variability in procedure time. To show the applicability of the approach, the model was used to automatically predict surgical end-times.
methodsA dataset of 40 video-recorded LHs was manually annotated for instrument use and divided into ten surgical phases. The use of instruments provided the feature input for building a Random Forest surgical phase recognition model that was trained to automatically recognise surgical phases. Tenfold cross-validation was performed to optimise the model for predicting the surgical end-time throughout the procedure.
resultsAverage surgery time is 128 ± 27 min. Large variability within specific phases is seen. Overall, the Random Forest model reaches an accuracy of 77% recognising the current phase in the procedure. Six of the phases are predicted accurately over 80% of their duration. When predicting the surgical end-time, on average an error of 16 ± 13 min is reached throughout the procedure.
conclusionsThis study demonstrates an intra-operative approach to recognise surgical phases in 40 laparoscopic hysterectomy cases based on instrument usage data. The model is capable of automatic detection of surgical phases for generation of a solid prediction of the surgical end-time.
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