Vinayak Rengan, Pravin Meenashi Sundaram, Eham Arora, Sabari Girieasen, Ashvind Bawa, Naveen Alexander, Rengan Ravanasamudram Sitaraman, Vimalakar Reddy, Vishakha Kalikar, Aman Arora, Lakshmi Kona, Rochita Venkataramanan, Devansh Lalwani, Dakshin Meenashi Sundaram, Rohit Kalla
Deep 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.
BACKGROUND: Accurate 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.
METHODS: 215 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.
RESULTS: Automated 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.
CONCLUSION: Deep 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.