ArticleSurgical endoscopy2024
Automated segmentation of phases, steps, and tasks in laparoscopic cholecystectomy using deep learning.
Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Detection of anatomic landmarks during laparoscopic cholecystectomy with the use of artificial intelligence-a systematic review of the literature.Updates in surgery · 2026Pooled it
- Systematic review on the use of artificial intelligence to identify anatomical structures during laparoscopic cholecystectomy: a tool towards the future.Langenbeck's archives of surgery · 2025Pooled it
- Energy profile of laparoscopic TEP bilateral inguinal hernia repairs.Hernia : the journal of hernias and abdominal wall surgery · 2026Observational
- Deep learning-based semantic segmentation of abdominal wall layers during direct optical trocar insertion: toward AI-assisted surgical access.Surgical endoscopy · 2026Article
- Artificial Intelligence for Automated Recognition of Hepatocystic Anatomy During Laparoscopic Cholecystectomy: Current Evidence, Clinical Readiness, and Future Directions.Medicina (Kaunas, Lithuania) · 2026Review
- External validation and clinical readiness of intraoperative video AI in general surgery: a systematic review.Surgery today · 2026Review
- Artificial intelligence in the recognition of stages in live laparoscopic cholecystectomy.Updates in surgery · 2026Article
- Development of a Realistic Physical Phantom for Laparoscopic and Robotic-Assisted Sacrocolpopexy Training and Associated.IEEE journal of translational engineering in health and medicine · 2026Article
- Remote detection of the critical view of safety in pediatric laparoscopic cholecystectomy using artifitial intelligence.World journal of pediatric surgery · 2026Article
- Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.Frontiers in digital health · 2026Review
- Renji endoscopic submucosal dissection video data set for Esophagus.Scientific data · 2025Article
- Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions.Scientific data · 2025Article
- Renji endoscopic submucosal dissection video data set for early gastric cancer.Scientific data · 2025Article
- Artificial intelligence based surgical support for experimental laparoscopic Nissen fundoplication.Frontiers in pediatrics · 2025Article
- Kinematic Adaptive Frame Recognition (KAFR): A Novel Framework for Video Segmentation via Frame Similarity and Surgical Tool Tracking.IEEE access : practical innovations, open solutions · 2025Article
- Deep learning-based surgical phase recognition in laparoscopic cholecystectomy.Annals of hepato-biliary-pancreatic surgery · 2024Article
- Laparoscopic image segmentation of the hepatobiliary system: Current status and advances from a deep learning perspective.Digital healthReview
- EgoSurgery-HTS: A Dataset for Egocentric Hand-Tool Segmentation in Open Surgery Videos.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundVideo-based review is paramount for operative performance assessment but can be laborious when performed manually. Hierarchical Task Analysis (HTA) is a well-known method that divides any procedure into phases, steps, and tasks. HTA requires large datasets of videos with consistent definitions at each level. Our aim was to develop an AI model for automated segmentation of phases, steps, and tasks for laparoscopic cholecystectomy videos using a standardized HTA.
methodsA total of 160 laparoscopic cholecystectomy videos were collected from a publicly available dataset known as cholec80 and from our own institution. All videos were annotated for the beginning and ending of a predefined set of phases, steps, and tasks. Deep learning models were then separately developed and trained for the three levels using a 3D Convolutional Neural Network architecture.
resultsFour phases, eight steps, and nineteen tasks were defined through expert consensus. The training set for our deep learning models contained 100 videos with an additional 20 videos for hyperparameter optimization and tuning. The remaining 40 videos were used for testing the performance. The overall accuracy for phases, steps, and tasks were 0.90, 0.81, and 0.65 with the average F1 score of 0.86, 0.76 and 0.48 respectively. Control of bleeding and bile spillage tasks were most variable in definition, operative management, and clinical relevance.
conclusionThe use of hierarchical task analysis for surgical video analysis has numerous applications in AI-based automated systems. Our results show that our tiered method of task analysis can successfully be used to train a DL model.
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