A Multi-Method Fusion for Improving Missing Teeth Detection in Intraoral Images

Monitoring Decayed, Missing, and Filled Teeth (DMFT) provides valuable insights into the prevalence and severity of tooth decay. However, existing algorithms often struggle with detecting missing teeth, as they can be overly sensitive to incomplete features. To address this limitation, this study proposes a novel YOLOv8 framework for tooth detection integrated with linear regression, DBSCAN clustering, and Lagrange interpolation for identifying missing teeth. A dataset of 983 frontal intraoral images acquired through smartphone-based imaging was considered. Experimenting on 197 images demonstrated that the proposed method enhances system performance, achieving precision of 64.27%, recall of 75.34%, and mean Average Precision (mAP) of 52.03%. Compared to the standard YOLOv8 model, the proposed approach improves precision by 2.27%, recall by 20.66%, and mAP by 6.83%. These findings highlight the potential of the proposed method as an effective tool for monitoring DMFT, contributing to improved diagnostic accuracy in enhancing missing teeth detection.