Automatic dental age calculation using orthopantomogram and deep learning

Abstract

Abstract Background: Dental age is crucial for treatment planning in pediatric and orthodontic dentistry. Dental age calculation methods can be categorized into morphological, biochemical, and radiological methods. Radiological methods are commonly used because they are non-invasive and reproducible. When radiographs are available, dental age can be calculated by evaluating the developmental stage of permanent teeth and converting it into an estimated age using a table, or by measuring the length between some landmarks such as the tooth, root, or pulp, and substituting them into regression formulas. However, these methods heavily depend on manual classification or measurement and are time-consuming in daily clinical practice. In this study, we proposed a novel, completely automatic dental age calculation pipeline from panoramic radiographs without time-consuming processes using various deep learning techniques. Methods: Overall, 8,023 panoramic radiographs were used as training data for Scaled-YOLOv4 to detect dental germs. In total, 18,485 single-root and 16,313 multi-root dental germ images were used as training data for EfficientNetV2 M to classify the developmental stages of detected dental germs. 157 panoramic radiographs were used to compare automatic and manual human experts' dental age calculations. Results: Our dental germ detection was achieved with a mean average precision of 98.26, and dental germ classifier for single and multi root were achieved with a Top-3 accuracy of 98.46% and 98.36%, respectively. A mean absolute error of 0.261 years was achieved compared with human experts. Conclusion: Our novel pipeline is expected to support dentists by reducing time for dental age calculations with clinically acceptable performance.