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.

[Reducing the incidence of needlestick injuries due to insulin pen injectors among self-users in the outpatient clinic setting].

BACKGROUND & PROBLEM: The rising number of people practicing insulin self-injection at home has led to increasing numbers of needlestick injuries due to inadequate self-injection skills among these patients. To reduce needlestick injuries at home, patients should not recap needles and should adopt proper needle disposal practices. A random survey of 80 outpatients currently using insulin pen injectors at home conducted between February and April 2012 found that 70% self-reported suffering needlestick incidents. Data analysis indicated the principal causes of these incidents were the lack of standard operating procedures, the absence of educational training, the shortage of educational instruction sheets for patients, and the inadequate skills and tools available to patients for disposing of needles safely at home. PURPOSE: The aim of this project was to decrease the needlestick incidence rate for outpatients that use insulin pen injectors in order to increase overall patient safety. RESOLUTION: The project team established a pen injector standard operation procedure (SOP), conducted an educational training program, developed nursing instruction sheets for patients, designed and distributed needle disposal containers to patients, and taught patients the correct techniques for the disposing of needles at home. RESULTS: The needlestick incidence rate decreased from 70% pretest to 2.6% following implementation of the abovementioned measures. CONCLUSIONS: This project effectively reduced the needlestick rate attributable to insulin pen injectors. The authors hope that other departments will adopt this approach in order to improve patient safety.