Boosting Algorithm for Optimizing Morphological Dental Age Estimation Method: A Southern China Population Study

Abstract

Abstract Age estimation based on the mineralized morphology of teeth is one of the important elements of forensic anthropology. To explore the most suitable age assessment protocol for adolescents in the South China population, 1477 panoramic radiographs images of people aged 2–18 years in the South were collected and staged by the Demirjian mineralization staging method. The dental age were estimated using the parameters of the Demirjian and Willems. Mathematical optimization and machine learning optimization were also performed in the data processing process in an attempt to obtain a more accurate model. The results show that Willems' method is more accurate in estimating the dental age of the South China population, while the model can be further optimized by re-assigning the model through a non-intercept regression method. The machine learning model presented excellent results in terms of the efficacy comparison results with the traditional mathematical model, and the machine learning model under the Boosting framework such as Gradient Boosting Decision Tree (GBDT) significantly reduced the error in dental age estimation compared to the traditional mathematical method. This machine learning processing method based on traditional assessment data can effectively reduce the error of assessment while saving arithmetic power. This study demonstrates the effectiveness of the GBDT algorithm in optimizing forensic age estimation models and provides a reference for other regions to use this scheme for age assessment model architecture, also the lightweight nature of machine learning offers the possibility of widespread forensic anthropological age estimation.