AUTHOR=Rengan Vinayak , Meenashi Sundaram Pravin , Arora Eham , Girieasen Sabari , Bawa Ashvind , Alexander Naveen , Ravanasamudram Sitaraman Rengan , Reddy Vimalakar , Kalikar Vishakha , Arora Aman , Kona Lakshmi , Venkataramanan Rochita , Lalwani Devansh , Meenashi Sundaram Dakshin , Kalla Rohit 
  
TITLE=Deep learning in ventral hernia imaging: automated multi-structure CT segmentation for surgical planning
  
JOURNAL=Journal of Abdominal Wall Surgery
  
VOLUME=Volume 5 - 2026
  
YEAR=2026
  
URL=https://www.frontierspartnerships.org/journals/journal-of-abdominal-wall-surgery/articles/10.3389/jaws.2026.15545
  
DOI=10.3389/jaws.2026.15545
  
ISSN=2813-2092
  
ABSTRACT=BackgroundAccurate preoperative assessment of ventral hernia defects remains time-intensive and subject to inter-observer variability. Current manual CT analysis for surgical planning is time-consuming, with inconsistent measurements affecting operative decision-making.Methods215 CT scans of adults with ventral hernias were analyzed using TransUNet-inspired deep learning models. Expert annotations of anatomical landmarks and hernia features served as ground truth. Models were trained to automate segmentation of hernia defects and other critical anatomical structures.ResultsAutomated segmentation achieved IoU values of 0.85 for hernia defects, 0.89 for rectus abdominis muscles, 0.87 for lateral abdominal wall muscles, and 0.91 for psoas muscles.ConclusionDeep learning automation provides rapid, standardized hernia assessment for surgical planning. The system delivers objective measurements with significant time savings, demonstrating technical feasibility as a proof-of-concept that warrants further prospective clinical validation before deployment in operative decision-making.