简介：AbstractBackground:Joint dislocations significantly impact public health. However, a comprehensive study on the incidence, distribution, and risk factors for joint dislocations in China is lacking. We conducted the China National Joint Dislocation Study, which is a part of the China National Fracture Study conducted to obtain the national incidence and risk factors for traumatic fractures, and to investigate the incidence and risk factors for joint dislocations.Methods:For this national retrospective epidemiological study, 512,187 participants were recruited using stratified random sampling and probability-proportional-to-size method from January 19 to May 16, 2015. Participants who sustained joint dislocations of the trunk, arms, or legs (skull, sternum, and ribs being excluded) in 2014 were personally interviewed to obtain data on age, educational background, ethnic origin, occupation, geographic region, and urbanization degree. The joint-dislocation incidence was calculated based on age, sex, body site, and demographic factors. The risk factors for different groups were examined using multiple logistic regression.Results:One hundred and nineteen participants sustained 121 joint dislocations in 2014. The population-weighted incidence rate of joint dislocations of the trunk, arms, or legs was 0.22 (95% confidence interval [CI]: 0.16, 0.27) per 1000 population in 2014 (men, 0.27 [0.20, 0.34]; women, 0.16 [0.10, 0.23]). For all ages, previous dislocation history (male: OR 42.33, 95% confidence interval [CI]: 12.03–148.90; female: OR 54.43, 95% CI: 17.37–170.50) and alcohol consumption (male: OR 3.50, 95% CI: 1.49–8.22; female: OR 2.65, 95% CI: 1.08–6.50) were risk factors for joint dislocation. Sleeping less than 7 h/day was a risk factor for men. Compared with children, women aged ≥15 years (female 15–64 years: OR 0.16, 95% CI: 0.04–0.61; female ≥65 years: OR 0.06, 95% CI: 0.01–0.41) were less likely to sustain joint dislocations. Women with more than three children were at higher dislocation risk than women without children (OR 6.92, 95% CI: 1.18–40.78).Conclusions:The up-to-date data on joint dislocation incidence, distribution, and risk factors can be used as a reference for national healthcare, prevention, and management in China. Specific strategies for decreasing alcohol consumption and encouraging adequate sleeping hours should be developed to prevent or reduce dislocation incidents.Trial Registration:Chinese Clinical Trial Registry, ChiCTR-EPR-15005878.

简介：AbstractBackground:The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images.Methods:Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists.Results:The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%).Conclusions:The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registration:Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.