ArticleSurgical endoscopy2022
Artificial intelligence software available for medical devices: surgical phase recognition in laparoscopic cholecystectomy.
Article in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Automated surgical phase recognition in pediatric laparoscopic fundoplication using deep learning: a retrospective video-based study.Surgical endoscopy · 2026Article
- AI-based assessment of surgical efficiency and exposure quality in laparoscopic distal gastrectomy using a phase recognition model.Surgical endoscopy · 2026Article
- Automated surgical phase recognition and analysis in single-incision laparoscopic cholecystectomy using artificial intelligence.Surgical endoscopy · 2026Article
- Review
- Construction and validation of a high-precision annotated dataset for developing intelligent critical vein recognition models in laparoscopic pancreatic surgery.Frontiers in surgery · 2026Article
- An artificial intelligence-based image recognition model using indocyanine green cholangiography to identify the hepatocystic triangle during minimally‑invasive cholecystectomy.Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques · 2025Article
- Unveiling the Algorithm: The Role of Explainable Artificial Intelligence in Modern Surgery.Healthcare (Basel, Switzerland) · 2025Review
- Artificial intelligence-assisted scar visualization under intraoperative bleeding using CycleGAN and uncertainty fusion in laparoscopic cholecystectomy.Surgical endoscopy · 2025Article
- Artificial Intelligence in Gastrointestinal Surgery: A Systematic Review of Its Role in Laparoscopic and Robotic Surgery.Journal of personalized medicine · 2025Review
- Surgical skill assessment using an AI-based surgical phase recognition model for laparoscopic cholecystectomy.Surgical endoscopy · 2025Article
- Artificial intelligence for intraoperative video analysis in robotic-assisted esophagectomy.Surgical endoscopy · 2025Review
- Balancing Ethics and Innovation: Can Artificial Intelligence Safely Transform Emergency Surgery? A Narrative Perspective.Journal of clinical medicine · 2025Article
- Development of an artificial intelligence system to indicate intraoperative findings of scarring in laparoscopic cholecystectomy for cholecystitis.Surgical endoscopy · 2025Article
- Artificial intelligence-assisted phase recognition and skill assessment in laparoscopic surgery: a systematic review.Frontiers in surgery · 2025Review
- LC-YOLOmatch: a novel scene segmentation approach based on YOLO for laparoscopic cholecystectomy.Frontiers in artificial intelligence · 2025Article
- Real-time detection of active bleeding in laparoscopic colectomy using artificial intelligence.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
- Automatic surgical phase recognition-based skill assessment in laparoscopic distal gastrectomy using multicenter videos.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Article
- Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals.Postgraduate medical journal · 2023Review
- Development of an artificial intelligence system for real-time intraoperative assessment of the Critical View of Safety in laparoscopic cholecystectomy.Surgical endoscopy · 2023Article
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Authors and funding
13 authors.
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Abstract
backgroundSurgical process modeling automatically identifies surgical phases, and further improvement in recognition accuracy is expected with deep learning. Surgical tool or time series information has been used to improve the recognition accuracy of a model. However, it is difficult to collect this information continuously intraoperatively. The present study aimed to develop a deep convolution neural network (CNN) model that correctly identifies the surgical phase during laparoscopic cholecystectomy (LC).
methodsWe divided LC into six surgical phases (P1-P6) and one redundant phase (P0). We prepared 115 LC videos and converted them to image frames at 3 fps. Three experienced doctors labeled the surgical phases in all image frames. Our deep CNN model was trained with 106 of the 115 annotation datasets and was evaluated with the remaining datasets. By depending on both the prediction probability and frequency for a certain period, we aimed for highly accurate surgical phase recognition in the operation room.
resultsNine full LC videos were converted into image frames and were fed to our deep CNN model. The average accuracy, precision, and recall were 0.970, 0.855, and 0.863, respectively.
conclusionThe deep learning CNN model in this study successfully identified both the six surgical phases and the redundant phase, P0, which may increase the versatility of the surgical process recognition model for clinical use. We believe that this model can be used in artificial intelligence for medical devices. The degree of recognition accuracy is expected to improve with developments in advanced deep learning algorithms.
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