Objectives: Deep learning methods have achieved impressive diagnostic performance in the field of radiology. The current study aimed to use deep learning methods to detect caries lesions, classify different radiographic extensions on panoramic films, and compare the classification results with those of expert dentists. Methods: A total of 1160 dental panoramic films were evaluated by three expert dentists. All caries lesions in the films were marked with circles, whose combination was defined as the reference dataset. A training and validation dataset (1071) and a test dataset (89) were then established from the reference dataset. A convolutional neural network, called nnU-Net, was applied to detect caries lesions, and DenseNet121 was applied to classify the lesions according to their depths (dentin lesions in the outer, middle, or inner third D1/2/3 of dentin). The performance of the test dataset in the trained nnU-Net and DenseNet121 models was compared with the results of six expert dentists in terms of the intersection over union (IoU), Dice coefficient, accuracy, precision, recall, negative predictive value (NPV), and F1-score metrics. Results: nnU-Net yielded caries lesion segmentation IoU and Dice coefficient values of 0.785 and 0.663, respectively, and the accuracy and recall rate of nnU-Net were 0.986 and 0.821, respectively. The results of the expert dentists and the neural network were shown to be no different in terms of accuracy, precision, recall, NPV, and F1-score. For caries depth classification, DenseNet121 showed an overall accuracy of 0.957 for D1 lesions, 0.832 for D2 lesions, and 0.863 for D3 lesions. The recall results of the D1/D2/D3 lesions were 0.765, 0.652, and 0.918, respectively. All metric values, including accuracy, precision, recall, NPV, and F1-score values, were proven to be no different from those of the experienced dentists. Conclusion: In detecting and classifying caries lesions on dental panoramic radiographs, the performance of deep learning methods was similar to that of expert dentists. The impact of applying these well-trained neural networks for disease diagnosis and treatment decision making should be explored.

Dental caries has been a common health issue throughout the world, which can even lead to dental pulp and root apical inflammation eventually. Timely and effective treatment of dental caries is vital for patients to reduce pain. Traditional caries disease diagnosis methods like naked-eye detection and panoramic radiograph examinations rely on experienced doctors, which may cause misdiagnosis and high time-consuming. To this end, we propose a novel deep learning architecture called CariesNet to delineate different caries degrees from panoramic radiographs. We firstly collect a high-quality panoramic radiograph dataset with 3127 well-delineated caries lesions, including shallow caries, moderate caries, and deep caries. Then we construct CariesNet as a U-shape network with the additional full-scale axial attention module to segment these three caries types from the oral panoramic images. Moreover, we test the segmentation performance between CariesNet and other baseline methods. Experiments show that our method can achieve a mean 93.64% Dice coefficient and 93.61% accuracy in the segmentation of three different levels of caries.

The aim of this study was to explore the protective effects of Salvia miltiorrhiza injection against learning and memory impairment in streptozotocin (STZ)-induced diabetic rats and the possible mechanism involved. Sprague Dawley male rats (n=30) were randomized into three groups: Diabetes, diabetes treated with S. miltiorrhiza injection and normal control. Diabetes was induced by an intraperitoneal injection of STZ (65 mg/kg). The S. miltiorrhiza injection-treated rats received an intraperitoneal injection of S. miltiorrhiza (5 ml/kg/day) while the rats of the other two groups were administered an intraperitoneal injection of the same volume of 0.9% saline for four weeks. After four weeks of treatment, the escape latency and search strategies in the rats were assessed by the Morris water maze test. The protein levels of mitogen-activated protein kinase phosphatase-1 (MKP-1) were also assessed by immunohistochemistry. Four weeks after the induction of diabetes, the body weight of the diabetic rats was significantly lower and the blood glucose concentration was significantly higher than that of the control rats. S. miltiorrhiza injection was observed to improve the blood glucose and learning ability (P<0.05). Compared with the control group, the expression of MKP-1 was significantly decreased in the hippocampal area of the diabetes group; S. miltiorrhiza injection-treated rats showed an increased expression compared with the diabetic rats, but the expression remained lower than that of the normal control group (P<0.05). In conclusion, S. miltiorrhiza injection can improve the learning and memory decline of diabetic rats. The changes in expression of MKP-1 under hyperglycemia may play a role in the protective effects of S. miltiorrhiza against dementia in diabetic rats.