Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network.

OBJECTIVES: The purpose of this study was to automatically diagnose odontogenic cysts and tumors of both jaws on panoramic radiographs using deep learning. We proposed a novel framework of deep convolution neural network (CNN) with data augmentation for detection and classification of the multiple diseases. METHODS: We developed a deep CNN modified from YOLOv3 for detecting and classifying odontogenic cysts and tumors of both jaws. Our data set of 1282 panoramic radiographs comprised 350 dentigerous cysts (DCs), 302 periapical cysts (PCs), 300 odontogenic keratocysts (OKCs), 230 ameloblastomas (ABs), and 100 normal jaws with no disease. In addition, the number of radiographs was augmented 12-fold by flip, rotation, and intensity changes. We evaluated the classification performance of the developed CNN by calculating sensitivity, specificity, accuracy, and area under the curve (AUC) for diseases of both jaws. RESULTS: The overall classification performance for the diseases improved from 78.2% sensitivity, 93.9% specificity,91.3% accuracy, and 0.86 AUC using the CNN with unaugmented data set to 88.9% sensitivity, 97.2% specificity, 95.6% accuracy, and 0.94 AUC using the CNN with augmented data set. CNN using augmented data set had the following sensitivities, specificities, accuracies, and AUCs: 91.4%, 99.2%, 97.8%, and 0.96 for DCs, 82.8%, 99.2%, 96.2%, and 0.92 for PCs, 98.4%,92.3%,94.0%, and 0.97 for OKCs, 71.7%, 100%, 94.3%, and 0.86 for ABs, and 100.0%, 95.1%, 96.0%, and 0.97 for normal jaws, respectively. CONCLUSION: The CNN method we developed for automatically diagnosing odontogenic cysts and tumors of both jaws on panoramic radiographs using data augmentation showed high sensitivity, specificity, accuracy, and AUC despite the limited number of panoramic images involved.

Hyperglycemia decreases the expression of ATP synthase ß subunit and enolase 2 in glomerular epithelial cells.

Glomerular epithelial cells (GECs) are known to play a key role in maintaining the structure and function of the glomerulus. GEC injury induced by hyperglycemia is present in early-stage diabetic nephropathy (DN), which is the most common cause of renal failure. In an attempt to identify target proteins involved in the pathogenesis of GEC injury at early DN, we performed the proteomic analysis using primary cultures of GECs, prepared from the dissected rat glomeruli. The protein expression profiles in the two-dimensional electrophoresis gels were compared between GECs treated for three days with normal glucose (5 mM) and those with high glucose (30 mM) concentrations. These concentrations correspond to blood glucose concentrations under normoglycemia and hyperglycemia, respectively. Proteins with differential expression levels were identified using ESI-Q-TOF tandem mass spectrometry. The primary GECs cultured in hyperglycemic conditions showed cellular hypertrophy and increased production of reactive oxygen species, both of which reflect the GEC injury. Our proteomic analysis identified eight proteins with differential expression profiles, depending on glucose concentrations. Among them, we selected ATP synthase ß subunit and enolase 2 that are related to energy metabolism and are down-regulated under hyperglycemia, and confirmed that hyperglycemia decreased the expression levels of ATP synthase ß subunit and enolase 2 proteins by western blotting analysis. Hyperglycemia may impair mitochondrial function and alter glycolysis in GECs by down-regulating the expression of ATP synthase ß subunit and enolase 2. The present study may provide a better understanding of the pathogenic mechanisms of GEC injury in early DN.