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Related Experiment Video

Updated: Sep 17, 2025

Application of a New Mesh Fixation Method in Laparoscopic Incisional Hernia Repair

Application of a New Mesh Fixation Method in Laparoscopic Incisional Hernia Repair

Published on: December 23, 2022

Landmark display system for laparoscopic inguinal hernia repair using artificial intelligence.

Keita Sato¹, Yuto Ishikawa²

¹Department of Surgery, Ise Red Cross Hospital, 1-471-2 Funae, Ise City, Mie, Japan. gummi.chocolate.pine@hotmail.com.

Surgical Endoscopy

|June 30, 2025

Summary

An AI system accurately identified key anatomical landmarks during transabdominal preperitoneal repair (TAPP), potentially reducing chronic postoperative inguinal pain by visualizing critical structures and preventing nerve damage.

Keywords:

Artificial intelligence Inguinal hernia Laparoscopic surgery Nerve injury Segmentation Transabdominal preperitoneal repair

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Published on: July 4, 2019

Area of Science:

Surgical innovation
Artificial intelligence in medicine
Minimally invasive surgery

Background:

Chronic postoperative inguinal pain (CPIP) is a significant complication following inguinal hernia repair.
Transabdominal preperitoneal repair (TAPP) is widely used, but CPIP remains a concern.
Identifying anatomical landmarks, particularly within the trapezoid of disaster, is crucial for preventing nerve injury during TAPP.

Purpose of the Study:

To evaluate an AI-based system for displaying critical anatomical landmarks during TAPP.
To assess the AI system's accuracy in identifying the vas deferens, gonadal vessels, and inferior epigastric vessels.

Main Methods:

A Feature Pyramid Network (FPN) segmentation model using EfficientNetB7 was developed and trained on 3323 intraoperative endoscopic images.
The model was tested on 10 new TAPP cases, analyzing 62 randomly selected cases (73 hernias) from a larger cohort.
Expert surgeons validated the AI system's performance in identifying key anatomical landmarks on surgical images.

Main Results:

The AI model achieved Dice coefficients of 0.67-0.70 for identifying the vas deferens, gonadal vessels, and inferior epigastric vessels.
The system successfully displayed anatomical landmarks on intraoperative images.
Expert evaluation confirmed 90% accuracy in landmark recognition, with low rates of postoperative pain (5.4% NRS ≥ 1, 2.2% CPIP NRS ≥ 3).

Conclusions:

The AI model demonstrates anatomical validity and accuracy in identifying essential landmarks during TAPP.
Visualizing these key structures with AI may enhance surgical safety by minimizing the risk of nerve damage.
This technology holds promise for improving patient outcomes and reducing the incidence of CPIP.