Hierarchical Machine Vision application for Automated Diagnosis of Dental X-Ray Images

Abstract

This study proposes a unified machine vision framework for the automated analysis of dental panoramic X-ray images, addressing a critical intersection between artificial intelligence and healthcare operations. We developed a deep learning pipeline that integrates advanced models to perform tooth detection, segmentation, and disease classification within a single workflow. By converting medical images into a rich source of diagnostic information, this research enhances the clinical utility of existing imaging practices. From a managerial perspective, the integration of AI enables scalable, real-time diagnostics that can reduce clinician workload, support clinical decision-making, and improve resource allocation. Furthermore, the framework has the potential to expand access to dental care through telemedicine applications, particularly in underserved regions. This research lays the foundation for the development of AI-driven diagnostic tools that align with broader goals in healthcare innovation and digital transformation.