In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.

Reports showing high recurrence rates for intralesional curettage and bone grafting have made the current treatment principle for fibrous dysplasia controversial. This study aimed to report the postoperative clinical outcomes from three minimally invasive surgical strategies we use for monostotic fibrous dysplasia (MFD). Twelve patients with MFD presenting with no pathologic fracture or deformity and treated with one of three surgical strategies—plain open biopsy, plain alpha-tricalcium phosphate (ATP) reconstruction, and prophylactic bridge plating—were included. There were nine men and three women, with median age of 38 years. Mean follow-up was 88 weeks. Five cases involved the proximal femur, two each involved the femoral and tibial diaphyses, and one each involved the distal humerus, radial diaphysis, and proximal tibia. All cases were reviewed for functional and radiological outcomes. Median time to full activity was 1 day (range 1 to 3) for the plain open biopsy group, while the prophylactic bridge-plating and plain ATP reconstruction groups had longer median recovery times (59 days, range 3 to 143, and 52 days, range 11 to 192, respectively). Musculoskeletal Tumor Society scores at last follow-up were excellent for all the cases (mean 29.6, range 25 to 30). Radiological analysis using Gaski et al.’s criteria showed plain open biopsy resulted in partial resolution of proximal femoral lesions, while ATP reconstruction and prophylactic plating resulted in no change and progression in this lesion site, respectively. For femoral diaphyseal lesions, prophylactic plating resulted in partial resolution, while ATP reconstruction resulted in no change. In the tibial diaphysis, prophylactic plating resulted in partial resolution, while plain open biopsy resulted in no change. For the lesions involving the distal humerus and the proximal tibia, plain open biopsy resulted in partial resolution, while for the radial diaphyseal lesion, ATP reconstruction resulted in no change. Radiological progression was limited in 11 (92%) cases, and none had postoperative complications. Plain open biopsies for asymptomatic lesions; prophylactic bridge plating for symptomatic, large diaphyseal lytic lesions; and plain ATP reconstructions for both small and large nondiaphyseal symptomatic lytic lesions may be acceptable alternatives to curettage-incorporating procedures for MFD.

Although the number of patients with osteoporosis is increasing worldwide, diagnosis and treatment are presently inadequate. In this study, we developed a deep learning model to predict bone mineral density (BMD) and T-score from chest X-rays, which are one of the most common, easily accessible, and low-cost medical imaging examination methods. The dataset used in this study contained patients who underwent dual-energy X-ray absorptiometry (DXA) and chest radiography at six hospitals between 2010 and 2021. We trained the deep learning model through ensemble learning of chest X-rays, age, and sex to predict BMD using regression and T-score for multiclass classification. We assessed the following two metrics to evaluate the performance of the deep learning model: (1) correlation between the predicted and true BMDs and (2) consistency in the T-score between the predicted class and true class. The correlation coefficients for BMD prediction were hip = 0.75 and lumbar spine = 0.63. The areas under the curves for the T-score predictions of normal, osteopenia, and osteoporosis diagnoses were 0.89, 0.70, and 0.84, respectively. These results suggest that the proposed deep learning model may be suitable for screening patients with osteoporosis by predicting BMD and T-score from chest X-rays.

Rhabdomyosarcoma, the most common soft tissue sarcoma noted in childhood, requires multimodality treatment, including chemotherapy, surgical resection, and/or radiation therapy. The majority of the patients with localized rhabdomyosarcoma can be cured; however, the long-term outcomes in patients with metastatic rhabdomyosarcoma remain poor. The standard chemotherapy regimen for patients with rhabdomyosarcoma is the combination of vincristine, actinomycin, and cyclophosphamide/ifosfamide. In recent clinical trials, modifications of the standard chemotherapy protocol have shown improvements in the outcomes in patients with rhabdomyosarcoma. In various type of malignancies, new treatments, such as molecular targeted drugs and immunotherapies, have shown superior clinical outcomes compared to those of standard treatments. Therefore, it is necessary to assess the benefits of these treatments in patients with rhabdomyosarcoma. Moreover, recent basic and clinical studies on rhabdomyosarcoma have reported promising therapeutic targets and novel therapeutic approaches. This article reviews the recent challenges and advances in the management of rhabdomyosarcoma.