Automated Diagnosis of Dental Conditions in Panoramic X-Rays with Ensemble Deep Learning Models

Over 85% of clinical dental treatment decisions are based on radiographic interpretation, recent research has shown. This holds particularly for conditions that cannot be easily diagnosed during a routine check-up. Focusing on common pathologies in dentistry (such as caries and impacted teeth) and restorative materials (such as fillings and implants), this research recommends applying deep learning to automatically interpret panoramic dental X-ray images. Correct treatment planning and long-term patient management rely upon accurate detection of such conditions.Manual interpreting of radiographs is time consuming and prone to human errors. Some of the advanced deep learning methods our approach employs to address this challenge and deliver fast, reliable, and precise image analysis include Convolutional Neural Networks (CNNs), transfer learning, and ensemble modelling. We enhance classification performance and stability by adopting ensemble methods that tap into the strengths of multiple deep models.The system, being more than 92% accurate in its classification, had a potential for advancing oral healthcare using AI, enhancing clinical outcomes, and minimizing diagnostic delays.