ArticleSurgical endoscopy2024
Deep learning-based vessel automatic recognition for laparoscopic right hemicolectomy.
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 16 papers.
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16 citing papers in PubMed, 20 citations in OpenAlex.
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- Artificial-Intelligence-based Surgical Phase Recognition in Robot-Assisted Radical Prostatectomy and Cross-Surgeon Validation.Annals of surgical oncology · 2026Article
- Two decades of artificial intelligence in colon cancer diagnosis and treatment: a bibliometric analysis of research trends (2003-2023).Updates in surgery · 2026Review
- Semantic segmentation deep learning model boosts surgeons' organ recognition in minimally invasive hysterectomy - a prospective multi-center reader performance study using pre-selected video clips.International journal of surgery (London, England) · 2026Article
- Deep learning-assisted near-real-time identification of the inferior mesenteric artery and vein during laparoscopic rectal resection: a single-center proof-of-concept study.Frontiers in medicine · 2026Article
- Assessment of AI-driven anatomical recognition in robotic gastrectomy: A multicenter retrospective analysis.Surgical endoscopy · 2025Article
- Artificial Intelligence in Gastrointestinal Surgery: A Systematic Review of Its Role in Laparoscopic and Robotic Surgery.Journal of personalized medicine · 2025Review
- Deep learning-based vessel and nerve recognition model for lateral lymph node dissection: a retrospective feasibility study.Langenbeck's archives of surgery · 2025Article
- Deep learning-based gastrocolic trunk recognition in laparoscopic right hemicolectomy.Surgical endoscopy · 2025Article
- Feasibility of Simultaneous Artificial Intelligence-Assisted and NIR Fluorescence Navigation for Anatomical Recognition in Laparoscopic Colorectal Surgery.Journal of fluorescence · 2025Article
- Treatment of giant choledochal cysts with combined surgery and percutaneous transhepatic cholangial drainage: A case report.World journal of gastrointestinal surgery · 2025Article
- Vision techniques for anatomical structures in laparoscopic surgery: a comprehensive review.Frontiers in surgery · 2025Review
- Usefulness of an Artificial Intelligence Model in Recognizing Recurrent Laryngeal Nerves During Robot-Assisted Minimally Invasive Esophagectomy.Annals of surgical oncology · 2024Article
- Deep Learning in Spinal Endoscopy: U-Net Models for Neural Tissue Detection.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundIn laparoscopic right hemicolectomy (RHC) for right-sided colon cancer, accurate recognition of the vascular anatomy is required for appropriate lymph node harvesting and safe operative procedures. We aimed to develop a deep learning model that enables the automatic recognition and visualization of major blood vessels in laparoscopic RHC. MATERIALS AND
methodsThis was a single-institution retrospective feasibility study. Semantic segmentation of three vessel areas, including the superior mesenteric vein (SMV), ileocolic artery (ICA), and ileocolic vein (ICV), was performed using the developed deep learning model. The Dice coefficient, recall, and precision were utilized as evaluation metrics to quantify the model performance after fivefold cross-validation. The model was further qualitatively appraised by 13 surgeons, based on a grading rubric to assess its potential for clinical application.
resultsIn total, 2624 images were extracted from 104 laparoscopic colectomy for right-sided colon cancer videos, and the pixels corresponding to the SMV, ICA, and ICV were manually annotated and utilized as training data. SMV recognition was the most accurate, with all three evaluation metrics having values above 0.75, whereas the recognition accuracy of ICA and ICV ranged from 0.53 to 0.57 for the three evaluation metrics. Additionally, all 13 surgeons gave acceptable ratings for the possibility of clinical application in rubric-based quantitative evaluations.
conclusionWe developed a DL-based vessel segmentation model capable of achieving feasible identification and visualization of major blood vessels in association with RHC. This model may be used by surgeons to accomplish reliable navigation of vessel visualization.
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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.