GeriAIGastroNet: AI-Assisted Gastrointestinal Polyp Segmentation and Severity-Based Triage for Tele-Gastroenterology in Underserved Geriatric Populations

Background/Objectives: Colorectal cancer is a leading cause of cancer-related mortality worldwide, and early detection of gastrointestinal (GI) polyps through endoscopy is critical for improving patient outcomes. However, access to specialist gastroenterology care remains severely limited in Federal Health Professional Shortage Areas (HPSAs), particularly for high-acuity geriatric patients. This study proposes GeriAIGastroNet, a clinically oriented deep learning framework designed to support AI-assisted tele-gastroenterology workflows in resource-limited settings, with the primary objective of enabling AI-powered risk stratification and colonoscopy referral triage for elderly patients who lack on-site gastroenterology access. Methods: The framework integrates an EfficientNet-B4 backbone with multi-scale attention fusion and a geriatric severity-aware classification head to enable accurate GI polyp segmentation and automated clinical risk stratification from endoscopic images. Patients identified as high-risk are referred to colonoscopy-capable centers; such centers typically offer diagnostic colonoscopy with polypectomy capability for smaller and intermediate-complexity polyps, while patients with larger, sessile, or morphologically complex lesions requiring advanced endoscopic resection (e.g., endoscopic mucosal resection or endoscopic submucosal dissection) are further referred to tertiary endoscopy centers with specialized expertise. The model was trained and evaluated on the publicly available HyperKvasir dataset (1000 annotated polyp images). Results: GeriAIGastroNet achieved a classification accuracy of 96.77%, F1-score of 96.90%, Dice coefficient of 89.18%, and Intersection over Union (IoU) of 80.80%, outperforming established baselines, including U-Net, Attention U-Net, TransUNet, and Hybrid CNN-Transformer architectures. The integrated tele-gastroenterology decision support layer enables severity-based patient triage and automated referral triggering. Conclusions: These results demonstrate the potential of AI-powered polyp analysis to strengthen equitable access to GI care by facilitating risk stratification and specialist referral in HPSAs where direct endoscopy is unavailable, making the system deployable in telehealth infrastructures serving underserved elderly populations.