Artificial intelligence application and performance in forensic age estimation with mandibular third molars on radiographs

Background/Aim: Age estimation is of great importance due to legal requirements. Although there are many methods used, most of them are based on age related dental changes. Artificial intelligence based programs, one of the most current and popular topics in recent years, are becoming more and more important in dental studies. This study aims to measure the performance of deep learning in forensic age estimation from mandibular third molars using panoramic radiographs. Material and Methods: In our study, panoramic radiographs of male and female patients between the ages of 16-26 years who applied to our department for various reasons were used. The pixel-based Convolutional Neural Networks (CNN) method, one of the types of artificial neural networks, was applied. The high performance ResNeXt-101 model and Adamax algorithm were selected. The learning rate was set to 0.001. The dataset was labeled with the DentiAssist platform and randomly divided into 80% training and 20% testing. 1296 data under 18 and 1036 data over 18 were used. Dropout method was applied in case of over memorization. In the last step of the hidden layer, a linear two-class prediction was obtained using a structured fully connected layer. Results: The performance metrics for the ResNeXt neural network were 4.36% accuracy, 83.95% precision, 84.56% recall, 84.56% F1-score and 84.14% F1-score (80% confidence interval) when adequate training was provided. Conclusions: Artificial intelligence, which eliminates the subjective margin of error compared to conventional methods and rapidly processes a large amount of data, has achieved promising results in forensic age determination.