AI-Assisted Early Detection of Pancreatic Cancer Using Non- Contrast CT Scan

Despite a 5-year survival rate of approximately 10%, early detection of pancreatic cancer (particularly stage T1B tumors) significantly improves results. We present you an AI system for detecting pancreatic cancer with noncontradictory CT scans that are safer for patients with kidney problems and contrast allergies but challenges due to limited contrast. The system uses 3D-U-NET with a visual transformer classifier for segmentation together. Trained from the pancreatic CT dataset and MICCAI 2015 request data, this impressive metric achieves a cube-like coefficient of 0.94 for the pancreas and a cube-like coefficient of 0.91 with tumor segmentation with sensitivity of 0.95 or 0.92. To overcome the challenges of limited annotated data records and low contrast, we implement target lesion recognition algorithms and special data magnification techniques for medical imaging data. This system simultaneously highlights chromogenic tumors for radiologist interpretation and preserves the natural CT appearance that supports both DICOM and Nifti formats for clinical integration. With an inference period of approximately 9 seconds per scan, the system allows for actual time detection, improving diagnostic accuracy and reduced artificial errors. It also sets up a community platform for collaboration between healthcare professionals, researchers and patients, allowing for continuous model tanning through clinical feedback. This study shows that deep learning through trans-architectures improves tumor identification in challenging imaging conditions and provides a practical solution for the detection of early pancreatic cancer in clinical practice.