ArticleHernia : the journal of hernias and abdominal wall surgery2022
Automatic surgical phase recognition in laparoscopic inguinal hernia repair with artificial intelligence.
Article in Hernia : the journal of hernias and abdominal wall surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Artificial intelligence in the operating room: A systematic review of AI models for surgical phase, instruments and anatomical structure identification.Acta obstetricia et gynecologica Scandinavica · 2025Pooled it
- Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review.Hernia : the journal of hernias and abdominal wall surgery · 2024Pooled it
- Artificial intelligence in abdominal wall hernia surgery: clinical applications and translational readiness.Surgical endoscopy · 2026Review
- A proof-of-concept study of surgical-VLM for surgical support in robotic surgery: contextual benchmarking against ChatGPT-5.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- External validation and clinical readiness of intraoperative video AI in general surgery: a systematic review.Surgery today · 2026Review
- Artificial Intelligence-Based Surgical Phase Analysis Enables Objective Assessment of Surgeon Skill in Robotic Distal Gastrectomy: A Multicenter Study.Annals of gastroenterological surgery · 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
- Deep learning approach for critical exposure during division of the inferior mesenteric artery in colorectal surgery.Journal of robotic surgery · 2026Article
- Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.Frontiers in digital health · 2026Review
- Application of machine learning and deep learning in the diagnosis and treatment of inguinal hernia: a narrative review.Frontiers in medicine · 2026Review
- Role of Robotic Surgery and Artificial Intelligence in the Management of Abdominal Wall Hernias: A Systematic Review.Cureus · 2025Review
- Assessment of AI-driven anatomical recognition in robotic gastrectomy: A multicenter retrospective analysis.Surgical endoscopy · 2025Article
- External Validation of an Automated Surgical Step Recognition Model for Robotic Distal Gastrectomy (RDG) Using a Multicenter Dataset.Annals of gastroenterological surgery · 2025Article
- Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review.BJS open · 2025Article
- Artificial intelligence for intraoperative video analysis in robotic-assisted esophagectomy.Surgical endoscopy · 2025Review
- Artificial intelligence-assisted phase recognition and skill assessment in laparoscopic surgery: a systematic 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
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery.International journal of computer assisted radiology and surgery · 2024Article
- Evaluating surgical expertise with AI-based automated instrument recognition for robotic distal gastrectomy.Annals of gastroenterological surgery · 2024Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBecause of the complexity of the intra-abdominal anatomy in the posterior approach, a longer learning curve has been observed in laparoscopic transabdominal preperitoneal (TAPP) inguinal hernia repair. Consequently, automatic tools using artificial intelligence (AI) to monitor TAPP procedures and assess learning curves are required. The primary objective of this study was to establish a deep learning-based automated surgical phase recognition system for TAPP. A secondary objective was to investigate the relationship between surgical skills and phase duration.
methodsThis study enrolled 119 patients who underwent the TAPP procedure. The surgical videos were annotated (delineated in time) and split into seven surgical phases (preparation, peritoneal flap incision, peritoneal flap dissection, hernia dissection, mesh deployment, mesh fixation, peritoneal flap closure, and additional closure). An AI model was trained to automatically recognize surgical phases from videos. The relationship between phase duration and surgical skills were also evaluated.
resultsA fourfold cross-validation was used to assess the performance of the AI model. The accuracy was 88.81 and 85.82%, in unilateral and bilateral cases, respectively. In unilateral hernia cases, the duration of peritoneal incision (p = 0.003) and hernia dissection (p = 0.014) detected via AI were significantly shorter for experts than for trainees.
conclusionAn automated surgical phase recognition system was established for TAPP using deep learning with a high accuracy. Our AI-based system can be useful for the automatic monitoring of surgery progress, improving OR efficiency, evaluating surgical skills and video-based surgical education. Specific phase durations detected via the AI model were significantly associated with the surgeons' learning curve.
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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.