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Talwar et al. Art Int Surg. 2025;5:418-24 Artificial
DOI: 10.20517/ais.2025.22
Intelligence Surgery

Perspective Open Access

AI-powered medical imaging for ventral hernia repair

2
Ankoor Talwar 1 , Akshay I. Kelshiker , John P. Fischer 3
1
Department of Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03766, USA.
2
Department of Anesthesiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03766, USA.
3
Department of Surgery, University of Pennsylvania, Philadelphia, PA 19104, USA.
Correspondence to: Dr. Ankoor Talwar, Division of Plastic Surgery, Department of Surgery, Dartmouth-Hitchcock Medical
Center, 1 Medical Center Drive, Lebanon, NH 03766, USA. E-mail: ankoor.a.talwar@hitchcock.org
How to cite this article: Talwar A, Kelshiker AI, Fischer JP. AI-powered medical imaging for ventral hernia repair. Art Int Surg.
2025;5:418-24. https://dx.doi.org/10.20517/ais.2025.22

Received: 14 Mar 2025 First Decision: 23 Jun 2025 Revised: 29 Jun 2025 Accepted: 21 Jul 2025 Published: 4 Aug 2025

Academic Editors: Toby Collins, Gaya Spolverato Copy Editor: Xing-Yue Zhang Production Editor: Xing-Yue Zhang

Abstract
Ventral hernia repair (VHR) is the surgical restoration of abdominal wall integrity to correct hernia defects and
prevent recurrence. Artificial intelligence (AI) has emerged as a transformative tool in medical imaging, offering
novel solutions to enhance the workflow and outcomes in VHR. This manuscript explores AI-driven applications in
imaging for VHR, focusing on preoperative risk stratification, intraoperative augmented reality guidance, and
postoperative wound monitoring. AI imaging models have demonstrated efficacy in preoperatively predicting
hernia formation, optimizing surgical planning, and predicting complications. Recent advancements, including
convolutional neural networks and real-time object detection models, have shown promise in automating wound
assessment and streamlining clinical workflow. Still, there are notable challenges in AI imaging, such as dataset
bias, high computational demands, and model interpretability. Future work should prioritize dataset diversity,
computational efficiency, and explainable AI to ensure equitable, scalable, and clinically reliable AI imaging
integration for VHR.
Keywords: Artificial intelligence, ventral hernia repair, incisional hernia, deep learning, computer vision,
convolutional neural network, CT, photograph

INTRODUCTION
Ventral hernia repair (VHR) is a surgical procedure aimed at restoring the integrity of the abdominal wall

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
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