This study was performed as a part of serial experiments of applying convolutional neural network(CNN) in determining osteoporosis on panoramic radiograph. The purpose of this study was to investigate how sensitively CNN determine osteoporosis on cropped panoramic radiograph. Panoramic radiographs from 1268 female patients(mean age 45.2 ± 21.1 yrs) were selected for this study. For the osteoporosis group, 633(mean age 72.2 ± 8.5 yrs) were selected, while for the normal group 635(mean age 28.3 ± 7.0 yrs). AlexNet was utilized as CNN in this study. A multiple-column CNN was designed with two rectangular regions of interest on the mandible inferior area. An occluding method was used to analyze the sensitive area in determining osteoporosis on AlexNet. Testing of AlexNet showed accuracy of 99% in determining osteoporosis on panoramic radiographs. AlexNet was sensitive at the area of cortical and cancellous bone of the mandible inferior area including adjacent soft tissue.

Deep convolutional network is a deep learning approach to optimize image recognition. This study aimed to apply DCNN to the reading of mandibular cortical thinning in digital panoramic radiographs. Digital panoramic radiographs of 1,268 female dental patients (age 45.2 ± 21.1yrs) were used in the reading of the mandibular cortical bone by two maxillofacial radiologists. Among the subjects, 535 normal subject’s panoramic radiographs (age 28.6 ±7.4 yrs) and 533 those of osteoporosis pationts (age 72.1 ± 8.7 yrs) with mandibular cortical thinning were used for training DCNN. In the testing of mandibular cortical thinning, 100 panoramic radiographs of normal subjects (age 26.6 ± 4.5 yrs) and 100 mandibular cortical thinning (age 72.5 ± 7.2 yrs) were used. The sensitive area of DCNN to mandibular cortical thinning was investigated by occluding analysis. The readings of DCNN were compared by two maxillofacial radiologists. DCNN showed 97.5% accuracy, 96% sensitivity, and 99% specificity in reading mandibular cortical thinning. DCNN was sensitively responded on the cancellous and cortical bone of the mandibular inferior area. DCNN was effective in diagnosing mandibular cortical thinning.

The osteoblastic activity of carcinoma is restricted in osteoblastic metastasis, common in the patients with prostate cancer, whereas its mechanism and the factors involved are unknown. Here, we present a case of central adenocarcinoma showing the osteoblastic activity in the mandible of the 80-year-old Korean male who had suffered from the paresthesia of lower lip during four mouths. Clinically the overlying oral epithelium was intact, but the radiologic images revealed the ill-defined radiolucent intraosseous lesions in left ascending ramus. Microscopically, the mandibular lesion was composed with carcinoma of ductal or glandular differentiation but lack typical features of any epithelial salivary gland malignancies. Intriguingly abundant new bone formation was found in the stroma, but the tumor cells expressed no reactivity for prostate-specific antigen(PSA). The patient had low ionized calcium level, normal serum alkaline phosphatase and PSA level. Positron emission tomography-computed tomopraphy scan revealed the benign prostatic hyperplasia, but failed to trace the primary site of tumor other than mandible. Therefore, pathologically diagnosis for the lesion was informed as adenocarcinoma, not otherwise specified(NOS). Because occult primary tumor associated with osteoblastic metastasis cannot be completely ruled out, periodic and careful check-ups for the patient should be performed.