ArticleInternational journal of computer assisted radiology and surgery2024
Artificial intelligence for automatic surgical phase recognition of laparoscopic gastrectomy in gastric cancer.
Article in International journal of computer assisted radiology and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- AI-based assessment of surgical efficiency and exposure quality in laparoscopic distal gastrectomy using a phase recognition model.Surgical endoscopy · 2026Article
- Prediction of surgery start for automated anesthesia using draping detection from surveillance videos.Journal of clinical monitoring and computing · 2026Article
- Artificial Intelligence in Gastrointestinal Surgery: A Systematic Review of Its Role in Laparoscopic and Robotic Surgery.Journal of personalized medicine · 2025Review
- External Validation of an Automated Surgical Step Recognition Model for Robotic Distal Gastrectomy (RDG) Using a Multicenter Dataset.Annals of gastroenterological surgery · 2025Article
- [Translational challenges and clinical potential of artificial intelligence in minimally invasive surgery].Chirurgie (Heidelberg, Germany) · 2025Article
- Automated surgical action recognition and competency assessment in laparoscopic cholecystectomy: a proof-of-concept study.Surgical endoscopy · 2025Article
- Surgical step recognition in laparoscopic distal gastrectomy using artificial intelligence: a proof-of-concept study.Langenbeck's archives of surgery · 2024Article
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
purposeThis study aimed to classify laparoscopic gastric cancer phases. We also aimed to develop a transformer-based artificial intelligence (AI) model for automatic surgical phase recognition and to evaluate the model's performance using laparoscopic gastric cancer surgical videos.
methodsOne hundred patients who underwent laparoscopic surgery for gastric cancer were included in this study. All surgical videos were labeled and classified into eight phases (P0. Preparation. P1. Separate the greater gastric curvature. P2. Separate the distal stomach. P3. Separate lesser gastric curvature. P4. Dissect the superior margin of the pancreas. P5. Separation of the proximal stomach. P6. Digestive tract reconstruction. P7. End of operation). This study proposed an AI phase recognition model consisting of a convolutional neural network-based visual feature extractor and temporal relational transformer.
resultsA visual and temporal relationship network was proposed to automatically perform accurate surgical phase prediction. The average time for all surgical videos in the video set was 9114 ± 2571 s. The longest phase was at P1 (3388 s). The final research accuracy, F1, recall, and precision were 90.128, 87.04, 87.04, and 87.32%, respectively. The phase with the highest recognition accuracy was P1, and that with the lowest accuracy was P2.
conclusionAn AI model based on neural and transformer networks was developed in this study. This model can identify the phases of laparoscopic surgery for gastric cancer accurately. AI can be used as an analytical tool for gastric cancer surgical videos.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.