Automated Prediction of Postoperative Pancreatic Fistula Using Preoperative Computed Tomography
arXiv:2605.31539v1 Announce Type: cross Abstract: Postoperative pancreatic fistula (POPF) is a serious complication after pancreatic resection, increasing morbidity, hospital stay, and healthcare costs. We present an automatic, end-to-end deep learning pipeline-from pancreatic segmentation to classification-for preoperative POPF risk estimation and stratification using preoperative CT scans. A data set with auto-segmented pancreas volumes and surgical outcomes was used to evaluate multiple architectures, including a custom lightweight 3D CNN baseline (CNN3D), R(2+1)D ResNet-18, and ResNet-MC3-
Advances in deep learning and increasing accessibility to medical imaging data are enabling practical applications for AI in diagnostics and surgical risk assessment; widespread integration is happening quickly as new research proves efficacy.
This development indicates a tangible step towards AI-driven healthcare, potentially improving patient outcomes and reducing healthcare costs in complex surgical procedures through automated risk prediction.
Preoperative assessment for pancreatic resections can now incorporate automated AI analysis of CT scans to predict fistula risk, moving beyond traditional statistical models or surgeon intuition.
- · Patients undergoing pancreatic surgery
- · Healthcare providers
- · Medical AI companies
- · Healthcare systems
- · Traditional diagnostic methods
- · Surgical practices without AI integration
- · Medical imaging interpretation bottleneck
Reduced incidence and severity of postoperative complications in pancreatic surgery.
Increased adoption of AI in other complex surgical fields for risk prediction and personalized treatment planning.
Potential for reduced healthcare expenditures and improved resource allocation through preventative AI diagnostics across broad medical specialties.
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Read at arXiv cs.LG