Artificial intelligence may help identify osteoporosis on Dental panoramic radiographs
Osteoporosis is defined by the loss of bone mass and the deterioration of the microarchitecture of bone tissue. It is a common and potentially metabolic bone disease characterized by susceptibility to fracture.
Deep learning with the CNN model shows that osteoporosis can be classified with relatively higher accuracy from dental panoramic radiographs.This study demonstrates that CNNs can diagnose osteoporosis from dental panoramic radiographs with high levels of accuracy.
The study has been published in the Scientific Reports.
Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radiographs would be very cost-effective and useful. In this study, they investigated the use of deep learning to classify osteoporosis from dental panoramic radiographs. In addition, the effect of adding clinical covariate data to the radiographic images on the identification performance was assessed. For objective labelling, a dataset containing 778 images was collected from patients who underwent both skeletal-bone-mineral density measurement and dental panoramic radiography at a single general hospital between 2014 and 2020. Osteoporosis was assessed from the dental panoramic radiographs using convolutional neural network (CNN) models, including EfficientNet-b0, -b3, and -b7 and ResNet-18, -50, and -152. An ensemble model was also constructed with clinical covariates added to each CNN. The ensemble model exhibited improved performance on all metrics for all CNNs, especially accuracy and AUC.
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