AI-powered medical imaging for ventral hernia repair

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.