Comprehensive Analysis and Automation of Dental Treatment Outcomes: A Multi-Faceted Approach

This study investigates the effectiveness of various object identification models for dental diagnostics using Orthopantomograms (OPGs). A custom dataset was created specifically for dental image analysis, and the performance of several contemporary models was compared, including YOLOv5, YOLOv8, and multiple models built on the Detectron2 framework.Our findings reveal trade-offs between different models in terms of computing efficiency, false negative rate, total loss, and classification accuracy. These trade-offs are crucial considerations for researchers when selecting a model for a specific application in dental diagnostics. Notably, Faster R-CNN R50 FPN 3x achieved a balance between classification accuracy and data processing speed, while Mask R-CNN R50 DCS 3x demonstrated superior foreground classification accuracy despite a higher overall loss. Additionally, RPN R50 C4 emerged as the model with the fastest processing time. These results highlight the importance of tailoring model selection to the specific requirements of dental diagnostic tasks.This study is significant because it examines the success rate of advanced deep learning models in dental diagnostics, it could result in automated and precise detection of dental issues, benefiting patient care and minimizing manual workload for dental practitioners.