Background: Chest radiography is the standard investigation for identifying rib fractures. The application of artificial intelligence (AI) for detecting rib fractures on chest radiographs is limited by image quality control and multilesion screening. To our knowledge, few studies have developed and verified the performance of an AI model for detecting rib fractures by using multi-center radiographs. And existing studies using chest radiographs for multiple rib fracture detection have used more complex and slower detection algorithms, so we aimed to create a multiple rib fracture detection model by using a convolutional neural network (CNN), based on multi-center and quality-normalised chest radiographs. Methods: A total of 1080 radiographs with rib fractures were obtained and randomly divided into the training set (918 radiographs, 85%) and the testing set (162 radiographs, 15%). An object detection CNN, You Only Look Once v3 (YOLOv3), was adopted to build the detection model. Receiver operating characteristic (ROC) and free-response ROC (FROC) were used to evaluate the model’s performance. A joint testing group of 162 radiographs with rib fractures and 233 radiographs without rib fractures was used as the internal testing set. Furthermore, an additional 201 radiographs, 121 with rib fractures and 80 without rib fractures, were independently validated to compare the CNN model performance with the diagnostic efficiency of radiologists. Results: The sensitivity of the model in the training and testing sets was 92.0% and 91.1%, respectively, and the precision was 68.0% and 81.6%, respectively. FROC in the testing set showed that the sensitivity for whole-lesion detection reached 91.3% when the false-positive of each case was 0.56. In the joint testing group, the case-level accuracy, sensitivity, specificity, and area under the curve were 85.1%, 93.2%, 79.4%, and 0.92, respectively. At the fracture level and the case level in the independent validation set, the accuracy and sensitivity of the CNN model were always higher or close to radiologists’ readings. Conclusions: The CNN model, based on YOLOv3, was sensitive for detecting rib fractures on chest radiographs and showed great potential in the preliminary screening of rib fractures, which indicated that CNN can help reduce missed diagnoses and relieve radiologists’ workload. In this study, we developed and verified the performance of a novel CNN model for rib fracture detection by using radiography.

This paper develops an approach to speech-based emotion verification based on emotion variance modeling and discriminant scale-frequency maps. The proposed system consists of two parts-feature extraction and emotion verification. In the first part, for each sound frame, important atoms from the Gabor dictionary are selected by using the matching pursuit algorithm. The scale, frequency, and magnitude of the atoms are extracted to construct a nonuniform scale-frequency map, which supports auditory discriminability by the analysis of critical bands. Next, sparse representation is used to transform scale-frequency maps into sparse coefficients to enhance the robustness against emotion variance and achieve error-tolerance improvement. In the second part, emotion verification, two scores are calculated. A novel sparse representation verification approach based on Gaussian-modeled residual errors is proposed to generate the first score from the sparse coefficients. Such a classifier can minimize emotion variance and improve recognition accuracy. The second score is calculated by using the emotional agreement index (EAI) from the same coefficients. These two scores are combined to obtain the final detection result. Experiments on an emotional database of spoken speech were conducted and indicate that the proposed approach can achieve an average equal error rate (EER) of as low as 6.61%. A comparison among different approaches reveals that the proposed method is superior to the others and confirms its feasibility.

This article presents a knowledge mining model, where a city can plan its development based on existing knowledge during city expansion, for example, telecommunication resource allocation and crowd forecasts in a new region. Unlike most works that focused on Internet-of- Things (IoT) sensing, this study is aimed at urban planning by using harvested data, from the perspective of city architects. For large-scale metropolitan areas, a massive amount of data is generated every day, either from static surveys or dynamic IoT sensing. For urban planners, data collection is not their primary concern. How to transfer harvested knowledge from exiting parts of the city to suburban/rural/untapped areas is a new challenge. This is because those areas still lack sufficient statistics, and the density of IoT deployment is low. Therefore, development is risky and uncertain. To exploit new regions requires knowledge inference. Such a transition needs data interpretation from historical city dynamics, involving sensor deployment, human activities, and resource allocation in the vicinity. With the proposed model, a city can estimate the requirement for resources when the peripheral areas on the outskirts of a city develop. The same model can be applied to enterprises for resource deployment, and applications are not merely limited to governments.