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.

This study proved that MTF and CNR values have a significant association with subjective image quality. The diagnostic task should be considered in evaluation of CBCT image quality.

PurposeThe objective of this study was to investigate the relationships between primary implant stability as measured by impact response frequency and the structural parameters of trabecular bone using cone-beam computed tomography(CBCT), excluding the effect of cortical bone thickness.MethodsWe measured the impact response of a dental implant placed into swine bone specimens composed of only trabecular bone without the cortical bone layer using an inductive sensor. The peak frequency of the impact response spectrum was determined as an implant stability criterion (SPF). The 3D microstructural parameters were calculated from CT images of the bone specimens obtained using both micro-CT and CBCT.ResultsSPF had significant positive correlations with trabecular bone structural parameters (BV/TV, BV, BS, BSD, Tb.Th, Tb.N, FD, and BS/BV) (P<0.01) while SPF demonstrated significant negative correlations with other microstructural parameters (Tb.Sp, Tb.Pf, and SMI) using micro-CT and CBCT (P<0.01).ConclusionsThere was an increase in implant stability prediction by combining BV/TV and SMI in the stepwise forward regression analysis. Bone with high volume density and low surface density shows high implant stability. Well-connected thick bone with small marrow spaces also shows high implant stability. The combination of bone density and architectural parameters measured using CBCT can predict the implant stability more accurately than the density alone in clinical diagnoses.

PurposeThe purpose of this study was to examine whether periodontal pocket could be satisfactorily visualized by optical coherence tomography (OCT) and to suggest quantitative methods for measuring periodontal pocket depth.MethodsWe acquired OCT images of periodontal pockets in a porcine model and determined the actual axial resolution for measuring the exact periodontal pocket depth using a calibration method. Quantitative measurements of periodontal pockets were performed by real axial resolution and compared with the results from manual periodontal probing.ResultsThe average periodontal pocket depth measured by OCT was 3.10±0.15 mm, 4.11±0.17 mm, 5.09±0.17 mm, and 6.05±0.21 mm for each periodontal pocket model, respectively. These values were similar to those obtained by manual periodontal probing.ConclusionsOCT was able to visualize periodontal pockets and show attachment loss. By calculating the calibration factor to determine the accurate axial resolution, quantitative standards for measuring periodontal pocket depth can be established regardless of the position of periodontal pocket in the OCT image.

PurposeThe aims of the present study were to compare the image quality and visibility of tooth cracks between conventional methods and swept-source optical coherence tomography (SS-OCT) and to develop an automatic detection technique for tooth cracks by SS-OCT imaging.MethodsWe evaluated SS-OCT with a near-infrared wavelength centered at 1,310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks. The reliability of the SS-OCT images was verified by comparing the crack lines with those detected using conventional methods. After performing preprocessing of the obtained SS-OCT images to emphasize cracks, an algorithm was developed and verified to detect tooth cracks automatically.ResultsThe detection capability of SS-OCT was superior or comparable to that of trans-illumination, which did not discriminate among the cracks according to depth. Other conventional methods for the detection of tooth cracks did not sense initial cracks with a width of less than 100 μm. However, SS-OCT detected cracks of all sizes, ranging from craze lines to split teeth, and the crack lines were automatically detected in images using the Hough transform.ConclusionsWe were able to distinguish structural cracks, craze lines, and split lines in tooth cracks using SS-OCT images, and to automatically detect the position of various cracks in the OCT images. Therefore, the detection capability of SS-OCT images provides a useful diagnostic tool for cracked tooth syndrome.