Artificial intelligence augmented imaging of pancreatic fluid collections in acute pancreatitis

Acute pancreatitis (AP) is a significant global health burden, with pancreatic fluid collections (PFCs) posing major diagnostic and therapeutic challenges. Distinguishing fluid-only pseudocysts from debris-containing walled-off necrosis is critical for management but is often difficult with conventional imaging. Contrast-enhanced computed tomography underestimates necrotic debris, while magnetic resonance imaging (MRI) is costly and time-consuming and endoscopic ultrasound is invasive. Artificial intelligence (AI), particularly deep learning and radiomics, is emerging as a powerful tool to overcome these limitations. AI algorithms can automate the segmentation of the pancreas and PFCs, provide objective quantification of necrotic debris and predict disease severity. Furthermore, AI-driven techniques can accelerate MRI acquisition times and potentially generate synthetic images, reducing scanner dependency. This review synthesizes AI's role in augmenting pancreatic imaging in PFC, covering its applications in segmentation and volumetry, image generation, outcome prediction and workflow optimization and discusses challenges and future directions for its clinical integration.