ArticleAnnals of surgical oncology2024
Usefulness of an Artificial Intelligence Model in Recognizing Recurrent Laryngeal Nerves During Robot-Assisted Minimally Invasive Esophagectomy.
Article in Annals of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in esophageal surgery: a systematic review.Journal of robotic surgery · 2025Pooled it
- AI-based anatomical recognition for bladder neck dissection in robot-assisted radical prostatectomy.BJU international · 2026Article
- Artificial intelligence-based anatomical segmentation in transabdominal preperitoneal repair of groin hernia.Hernia : the journal of hernias and abdominal wall surgery · 2026Article
- Automatic recognition of anatomical structures and surgical phases in robot-assisted minimally invasive esophagectomy (RAMIE) using deep learning: a retrospective cohort study.Surgical endoscopy · 2026Article
- Artificial intelligence-based anatomical recognition improves surgeon decision-making during robotic gastrectomy.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- An artificial intelligence-based navigation system for enhancing anatomical recognition and safety during laparoscopic cholecystectomy: a pilot study.International journal of surgery (London, England) · 2026Article
- Artificial Intelligence in Esophagectomy: A Systematic Review.Journal of clinical medicine · 2026Review
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Artificial-Intelligence-based Surgical Phase Recognition in Robot-Assisted Radical Prostatectomy and Cross-Surgeon Validation.Annals of surgical oncology · 2026Article
- Advances in Minimally Invasive Esophagectomy-An Overview of Recent Developments and a Novel Classification of Innovations in Treatment of Thoracic Esophageal Cancer.Medicina (Kaunas, Lithuania) · 2025Review
- The Integration of Artificial Intelligence into Robotic Cancer Surgery: A Systematic Review.Journal of clinical medicine · 2025Review
- Current advances and challenges in minimally invasive esophagectomy.International journal of clinical oncology · 2025Review
- Robotic-assisted versus conventional minimally invasive esophagectomy: a retrospective cohort study from a high-volume center.Journal of robotic surgery · 2025Article
- Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025Article
- Critical Insights on the Clinical and Technical Limitations of AI-Assisted Recurrent Laryngeal Nerve Recognition in Esophageal Surgery.Annals of surgical oncology · 2025Article
- Reply to: "Critical Insights on the Clinical and Technical Limitations of AI-Assisted RLN Recognition in Esophageal Surgery".Annals of surgical oncology · 2025Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecurrent laryngeal nerve (RLN) palsy is a common complication in esophagectomy and its main risk factor is reportedly intraoperative procedure associated with surgeons' experience. We aimed to improve surgeons' recognition of the RLN during robot-assisted minimally invasive esophagectomy (RAMIE) by developing an artificial intelligence (AI) model.
methodsWe used 120 RAMIE videos from four institutions to develop an AI model and eight other surgical videos from another institution for AI model evaluation. AI performance was measured using the Intersection over Union (IoU). Furthermore, to verify the AI's clinical validity, we conducted the two experiments on the early identification of RLN and recognition of its location by eight trainee surgeons with or without AI.
resultsThe IoUs for AI recognition of the right and left RLNs were 0.40 ± 0.26 and 0.34 ± 0.27, respectively. The recognition of the right RLN presence in the beginning of right RLN lymph node dissection (LND) by surgeons with AI (81.3%) was significantly more accurate (p = 0.004) than that by surgeons without AI (46.9%). The IoU of right RLN during right RLN LND recognized by surgeons with AI (0.59 ± 0.18) was significantly higher (p = 0.010) than that by surgeons without AI (0.40 ± 0.29).
conclusionsSurgeons' recognition of anatomical structures in RAMIE was improved by our AI system with high accuracy. Especially in right RLN LND, surgeons could recognize the RLN more quickly and accurately by using the AI model.
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