Tooth Development Prediction Using a Generative Machine Learning Approach

Kokomoto K., Okawa R., Nakano K., et al. Tooth Development Prediction Using a Generative Machine Learning Approach. IEEE Access 12, 87645 (2024); https://doi.org/10.1109/ACCESS.2024.3416748.This study pioneers the use of generative deep learning in pediatric dentistry to predict dental growth using panoramic radiography, going beyond numerical analysis and providing dynamic representations of tooth development. We employed StyleGAN-XL, a state-of-the-art generative adversarial network (GAN), to generate realistic images of dental development stages in children. Our dataset consisted of 8,092 anonymized panoramic radiographs from Osaka University Dental Hospital containing various dentition stages and conditions. By interpolating latent vectors from primary or mixed dentition images with those from permanent dentition, we generated continuous transitioning images that visually represented the progression of dental development. The performance of the StyleGAN-XL model was evaluated using Fréchet inception distance scores. Pivotal tuning inversion was used to project real images onto the model's latent space, allowing us to effectively interpolate between current and future dental states. The resulting images showed a smooth transition from primary to permanent dentition, closely resembling the actual stages of dental development. This method represents a significant advancement in dental imaging and predictive analytics, offering a novel approach for clinicians and patients to visualize and understand dental growth. Our findings suggest broader applications for generative models in medical imaging, extending beyond traditional enhancement and modeling tasks. Our study highlights the transformative potential of GANs in medical imaging and provides a foundation for future advancements in predictive dentistry

Tooth Development Prediction Using a Generative Machine Learning Approach

This study pioneers the use of generative deep learning in pediatric dentistry to predict dental growth using panoramic radiography, going beyond numerical analysis and providing dynamic representations of tooth development. We employed StyleGAN-XL, a state-of-the-art generative adversarial network (GAN), to generate realistic images of dental development stages in children. Our dataset consisted of 8,092 anonymized panoramic radiographs from Osaka University Dental Hospital containing various dentition stages and conditions. By interpolating latent vectors from primary or mixed dentition images with those from permanent dentition, we generated continuous transitioning images that visually represented the progression of dental development. The performance of the StyleGAN-XL model was evaluated using Fréchet inception distance scores. Pivotal tuning inversion was used to project real images onto the model’s latent space, allowing us to effectively interpolate between current and future dental states. The resulting images showed a smooth transition from primary to permanent dentition, closely resembling the actual stages of dental development. This method represents a significant advancement in dental imaging and predictive analytics, offering a novel approach for clinicians and patients to visualize and understand dental growth. Our findings suggest broader applications for generative models in medical imaging, extending beyond traditional enhancement and modeling tasks. Our study highlights the transformative potential of GANs in medical imaging and provides a foundation for future advancements in predictive dentistry

Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object detection and image classification

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 time-consuming processes. In this study, we proposed a novel and completely automatic dental age calculation method using panoramic radiographs and deep learning techniques. Methods Overall, 8,023 panoramic radiographs were used as training data for Scaled-YOLOv4 to detect dental germs and mean average precision were evaluated. 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 and Top-3 accuracy was evaluated since the adjacent stages of the dental germ looks similar and the many variations of the morphological structure can be observed between developmental stages. Scaled-YOLOv4 and EfficientNetV2 M were trained using cross-validation. We evaluated a single selection, a weighted average, and an expected value to convert the probability of developmental stage classification to dental age. One hundred and fifty-seven panoramic radiographs were used to compare automatic and manual human experts’ dental age calculations. Results Dental germ detection was achieved with a mean average precision of 98.26% and dental germ classifiers for single and multi-root were achieved with a Top-3 accuracy of 98.46% and 98.36%, respectively. The mean absolute errors between the automatic and manual dental age calculations using single selection, weighted average, and expected value were 0.274, 0.261, and 0.396, respectively. The weighted average was better than the other methods and was accurate by less than one developmental stage error. Conclusion Our study demonstrates the feasibility of automatic dental age calculation using panoramic radiographs and a two-stage deep learning approach with a clinically acceptable level of accuracy

Japanese nationwide survey of hypophosphatasia reveals prominent differences in genetic and dental findings between odonto and non-odonto types.

Hypophosphatasia (HPP) is a rare and intractable metabolic bone disease caused by mutations in the ALPL gene. Here, we undertook a nationwide survey of HPP in Japan, specifically regarding the prominent genetic and dental manifestations of odonto (n = 16 cases) and other (termed "non-odonto") (n = 36 cases) types. Mean serum alkaline phosphatase (ALP) values in odonto-type patients were significantly greater than those of non-odonto-type patients (P<0.05). Autosomal dominant and autosomal recessive inheritance patterns were detected, respectively, in 89% of odonto-type and 96% of non-odonto-type patients. The ALPL "c.1559delT" mutation, associated with extremely low ALP activity, was found in approximately 70% of cases. Regarding dental manifestations, all patients classified as odonto-type showed early exfoliation of the primary teeth significantly more frequently than patients classified as non-odonto-type (100% vs. 56%; P<0.05). Tooth hypomineralisation was detected in 42% of non-odonto-type patients, but not in any odonto-type patients (0%; P<0.05). Collectively, these results suggest that genetic and dental manifestations of patients with odonto-type and non-odonto-type HPP are significantly different, and these differences should be considered during clinical treatment of patients with HPP