Efficacy of machine learning assisted dental age assessment in local population.

INTRODUCTION: Although the dental age assessment is commonly applied in forensic and maturity evaluation, the long-standing dilemma from population differences has limited its application. OBJECTIVES: This study aimed to verify the efficacy of the machine learning (ML) to build up the dental age standard of a local population. METHODS: We retrospectively studied 2052 panoramic films retrieved from healthy Taiwanese children aged 2.6-17.7 years with comparable sizes in each age-group. The recently reported Han population-based standard (H method) served as the control condition. To develop and validate ML models, random divisions of the sample in an 80%-20% ratio repeated 20 times. The model performances were compared with the H method, Demirjian's method, and Willems's method. RESULTS: The ML-assisted models provided more accurate age prediction than those non-ML-assisted methods. The range of errors was effectively reduced to less than one per year in the ML models. Furthermore, the consistent agreements among the age groups from preschool to adolescence were reported for the first time. The Gaussian process regression was the best ML model; of the non-ML modalities, the H method was the most efficacious, followed by the Demirjian's method and Willems's methods. CONCLUSION: The ML-assisted dental age assessment is helpful to provide customized standards to a local population with more accurate estimations in preschool and adolescent age groups than do studied conventional methods. In addition, the earlier complete tooth developments were also observed in present study. To construct more reliable dental maturity models in the future, additional environment-related factors should be taken into account.

The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay.

BACKGROUND: This study aimed to reveal the efficacy of the artificial intelligence (AI)-assisted dental age (DA) assessment in identifying the characteristics of growth delay (GD) in children. METHODS: The panoramic films matching the inclusion criteria were collected for the AI model training to establish the population-based DA standard. Subsequently, the DA of the validation dataset of the healthy children and the images of the GD children were assessed by both the conventional methods and the AI-assisted standards. The efficacy of all the studied modalities was compared by the paired sample t-test. RESULTS: The AI-assisted standards can provide much more accurate chronological age (CA) predictions with mean errors of less than 0.05 years, while the traditional methods presented overestimated results in both genders. For the GD children, the convolutional neural network (CNN) revealed the delayed DA in GD children of both genders, while the machine learning models presented so only in the GD boys. CONCLUSION: The AI-assisted DA assessments help overcome the long-standing populational limitation observed in traditional methods. The image feature extraction of the CNN models provided the best efficacy to reveal the nature of delayed DA in GD children of both genders.