ArticleJournal of healthcare engineering2021
Identification of the Vas Deferens in Laparoscopic Inguinal Hernia Repair Surgery Using the Convolutional Neural Network.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Artificial intelligence-based anatomical segmentation in transabdominal preperitoneal repair of groin hernia.Hernia : the journal of hernias and abdominal wall surgery · 2026Article
- Artificial intelligence in abdominal wall hernia surgery: clinical applications and translational readiness.Surgical endoscopy · 2026Review
- Role of Robotic Surgery and Artificial Intelligence in the Management of Abdominal Wall Hernias: A Systematic Review.Cureus · 2025Review
- Artificial Intelligence in Gastrointestinal Surgery: A Systematic Review of Its Role in Laparoscopic and Robotic Surgery.Journal of personalized medicine · 2025Review
- Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review.BJS open · 2025Article
- Vision techniques for anatomical structures in laparoscopic surgery: a comprehensive review.Frontiers in surgery · 2025Review
- Artificial Intelligence-What to Expect From Machine Learning and Deep Learning in Hernia Surgery.Journal of abdominal wall surgery : JAWS · 2024Review
- The Development of Artificial Intelligence in Hernia Surgery: A Scoping Review.Frontiers in surgery · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inguinal hernia repair is one of the most frequently conducted surgical procedures worldwide. Laparoscopic inguinal hernia repair is considered to be technically challenging. Artificial intelligence technology has made significant progress in medical imaging, but its application in laparoscopic surgery has not been widely carried out. Our aim is to detect vas deferens images in laparoscopic inguinal hernial repair using the convolutional neural network (CNN) and help surgeons to identify the vas deferens in time. We collected surgery videos from 35 patients with inguinal hernia who underwent laparoscopic hernia repair. We classified and labeled the images of the vas deferens and used the CNN to learn the image features. Totally, 2,600 images (26 patients) were labeled for training and validating the neural network and 1,200 images (6 patients) and 6 short video clips (3 patients) for testing. We adjusted the model parameters and tested the performance of the model under different confidence levels and IoU and used the chi-square to analyze the statistical difference in the video test dataset. We evaluated the model performance by calculating the true positive rate (TPR), true negative rate (TNR), accuracy (ACC), positive predictive value (PPV), and
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Registered trials
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