Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bladder (KUB) radiographs are inexpensive and frequently ordered in clinical practice. Thus, it is a potential screening tool for osteoporosis. In this study, we explored the possibility of predicting the bone mineral density (BMD) and classifying high-risk patient groups using KUB radiographs. We proposed DeepDXA-KUB, a deep learning model that predicts the BMD values of the left hip and lumbar vertebrae from an input KUB image. The datasets were obtained from Taiwanese medical centers between 2006 and 2019, using 8913 pairs of KUB radiographs and DXA examinations performed within 6 months. The images were randomly divided into training and validation sets in a 4:1 ratio. To evaluate the model's performance, we computed a confusion matrix and evaluated the sensitivity, specificity, accuracy, precision, positive predictive value, negative predictive value, F1 score, and area under the receiver operating curve (AUROC). Moderate correlations were observed between the predicted and DXA-measured BMD values, with a correlation coefficient of 0.858 for the lumbar vertebrae and 0.87 for the left hip. The model demonstrated an osteoporosis detection accuracy, sensitivity, and specificity of 84.7 %, 81.6 %, and 86.6 % for the lumbar vertebrae and 84.2 %, 91.2 %, and 81 % for the left hip, respectively. The AUROC was 0.939 for the lumbar vertebrae and 0.947 for the left hip, indicating a satisfactory performance in osteoporosis screening. The present study is the first to develop a deep learning model based on KUB radiographs to predict lumbar spine and femoral BMD. Our model demonstrated a promising correlation between the predicted and DXA-measured BMD in both the lumbar vertebrae and hip, showing great potential for the opportunistic screening of osteoporosis.

Diagnostic Accuracy of Deep Learning for the Prediction of Osteoporosis Using Plain X-rays: A Systematic Review and Meta-Analysis.

(1) Background: This meta-analysis assessed the diagnostic accuracy of deep learning model-based osteoporosis prediction using plain X-ray images. (2) Methods: We searched PubMed, Web of Science, SCOPUS, and Google Scholar from no set beginning date to 28 February 2023, for eligible studies that applied deep learning methods for diagnosing osteoporosis using X-ray images. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. The area under the receiver operating characteristic curve (AUROC) was used to quantify the predictive performance. Subgroup, meta-regression, and sensitivity analyses were performed to identify the potential sources of study heterogeneity. (3) Results: Six studies were included; the pooled AUROC, sensitivity, and specificity were 0.88 (95% confidence interval [CI] 0.85-0.91), 0.81 (95% CI 0.78-0.84), and 0.87 (95% CI 0.81-0.92), respectively, indicating good performance. Moderate heterogeneity was observed. Mega-regression and subgroup analyses were not performed due to the limited number of studies included. (4) Conclusion: Deep learning methods effectively extract bone density information from plain radiographs, highlighting their potential for opportunistic screening. Nevertheless, additional prospective multicenter studies involving diverse patient populations are required to confirm the applicability of this novel technique.

On-site Ultrasound Screens out Asymptomatic Knee Lesions in Elite Adolescent Male Basketball Players.

Basketball is a popular sport worldwide with a high injury risk. In this study, we conducted survey composed of clinical symptom reporting scale, physical examination and meticulous portable musculoskeletal ultrasound to 19 elite male high school basketball players and 15 regular male high school students. Our study showed the incidence of ultrasonographic findings of any lesion, suprapatellar effusion and proximal patellar tendinopathy is significantly higher in player group, and the incidence of asymptomatic ultrasonographic lesion is also higher in player group. Screening for asymptomatic lesions bares clinical relevance and plays a role in prevention of symptom development. With the concise and easy-to-perform ultrasonography protocol we performed and being interpreted by sports team physician, the protocol can offer precise diagnosis of common injury and screening for asymptomatic lesion potentially progressive.

Application of deep learning neural network in predicting bone mineral density from plain X-ray radiography.

DeepDXA is a deep learning model designed to infer bone mineral density data from plain pelvis X-ray, and it can achieve good predicted value for clinical use. PURPOSE: Osteoporosis is defined as a systemic disease of the bone characterized by a decrease in bone strength and deterioration of bone structure at the microscopic level, leading to bone fragility and increased risk of fracture. Bone mineral density (BMD) is the preferred method for the diagnosis of osteoporosis, and dual-energy x-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis. Conventional radiography is more suited for the screening of osteoporosis rather than diagnosis, and osteoporosis can be detected on radiographs by experienced physicians only. This study explored the possibility of predicting BMD relative to DXA using patient radiographs. METHODS: A deep learning algorithm of convolutional neural network (CNN) was used for the purpose. The method includes image segmentation, CNN learning, and a convolution-based regression model (DeepDXA) that links the isolated images of the femur bone to predict BMD value. Data were obtained in a single medical center from 2006 to 2018, with a total amount of 3472 pairs of pelvis X-ray and DXA examination within 1 year. RESULTS: The proposed workflow successfully predicted BMD values of the femur bone with the correlation coefficient (R) of 0.85 (P < 0.001) and the accuracy of 0.88 for prediction osteoporosis, a finding that could be reliably ready for further clinical use. CONCLUSION: When suspicious osteoporosis is seen on plain films using the deep learning method we developed, further referral to DXA for the definite diagnosis of osteoporosis is indicated.

Home-Based Orolingual Exercise Improves the Coordination of Swallowing and Respiration in Early Parkinson Disease: A Quasi-Experimental Before-and-After Exercise Program Study.

Introduction: The coordination of swallowing and respiration is important for safety swallowing without aspiration. This coordination was affected in Parkinson disease (PD). A noninvasive assessment tool was used to investigate the effect of an easy-to-perform and device-free home-based orolingual exercise (OLE) program on swallowing and respiration coordination in patients with early-stage PD. Materials and Methods: This study had a quasi-experimental before-and-after exercise program design. Twenty six patients with early-stage PD who were aged 62.12 ± 8.52 years completed a 12-week home-based OLE program. A noninvasive assessment tool was used to evaluate swallowing and respiration. For each patient, we recorded and analyzed 15 swallows (3 repeats of 5 water boluses: 1, 3, 5, 10, and 20 mL) before and after the home-based OLE program. Oropharyngeal swallowing and its coordination with respiration were the outcome measures. The frequency of piecemeal deglutition, pre- and post-swallowing respiratory phase patterns, and parameters of oropharyngeal swallowing and respiratory signals (swallowing respiratory pause [SRP], onset latency [OL], total excursion time [TET], excursion time [ET], second deflexion, amplitude, and duration of submental sEMG activity, and amplitude of laryngeal excursion) were examined. Results: The rate of piecemeal deglutition decreased significantly when swallowing 10- and 20-mL water boluses after the program. In the 1-mL water bolus swallowing trial, the rate of protective pre- and post-swallowing respiratory phase patterns was significantly higher after the program. For the parameters of oropharyngeal swallowing and respiratory signals, only the amplitude of laryngeal excursion was significantly lower after the program. Moreover, the volume of the water bolus significantly affected the SRP and duration of submental sEMG when patients swallowed three small water bolus volumes (1, 3, and 5 mL). Conclusion: The home-based OLE program improved swallowing and its coordination with respiration in patients with early-stage PD, as revealed using a noninvasive method. This OLE program can serve as a home-based program to improve swallowing and respiration coordination in patients with early-stage PD.