Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis in MR T2 Images

Motivation: Cervical spondylosis requires precise and automated diagnostic methods to handle the complexity of MRI-based T2-weighted assessments. Goal(s): To develop an expert-driven AI system for automated segmentation and diagnosis of cervical spondylosis using MR T2-weighted images. Approach: We introduce the PG-nnUNet model, which incorporates pathology-guided segmentation and edge loss for enhanced segmentation accuracy, and an expert-based framework for key clinical indicators. Results: The system achieved high accuracy in both segmentation and diagnostic tasks on MR T2 images, outperforming existing methods and showing reliability for clinical use. Impact: This AI-based framework enhances precision and efficiency in cervical spondylosis diagnosis, reducing clinician workload and improving diagnostic accuracy, with PG-nnUNet supporting consistent, automated decision-making. Future efforts will focus on multimodal imaging for broader applicability.