Artificial Intelligence Measurement of Preoperative Radiographs in Adolescent Idiopathic Scoliosis Based on Multiple-View Semantic Segmentation.

STUDY DESIGN: Cross-sectional study. OBJECTIVES: Imaging classification of adolescent idiopathic scoliosis (AIS) is directly related to the surgical strategy, but the artificial classification is complex and depends on doctors' experience. This study investigated deep learning-based automated classification methods (DL group) for AIS and validated the consistency of machine classification and manual classification (M group). METHODS: A total of 506 cases (81 males and 425 females) and 1812 AIS full spine images in the anteroposterior (AP), lateral (LAT), left bending (LB) and right bending (RB) positions were retrospectively used for training. The mean age was 13.6 ± 1.8. The mean maximum Cobb angle was 46.8 ± 12.0. U-Net semantic segmentation neural network technology and deep learning methods were used to automatically segment and establish the alignment relationship between multiple views of the spine, and to extract spinal features such as the Cobb angle. The type of each test case was automatically calculated according to Lenke's rule. An additional 107 cases of adolescent idiopathic scoliosis imaging were prospectively used for testing. The consistency of the DL group and M group was compared. RESULTS: Automatic vertebral body segmentation and recognition, multi-view alignment of the spine and automatic Cobb angle measurement were implemented. Compare to the M group, the consistency of the DL group was significantly higher in 3 aspects: type of lateral convexity (0.989 vs 0.566), lumbar curvature modifier (0.932 vs 0.738), and sagittal plane modifier (0.987 vs 0.522). CONCLUSIONS: Deep learning enables automated Cobb angle measurement and automated Lenke classification of idiopathic scoliosis whole spine radiographs with higher consistency than manual measurement classification.

The simulation, regulation capacity assessment and coping strategy of rainstorm runoff waterlogging in Zhu pai-chong Basin of Nanning, China.

Currently, China is experiencing a phase of rapid urbanization. With the frequent occurrence of extreme rainfall events within the context of climate change, the problem of heavy rainfall and waterlogging in many cities is very prominent. In November 2020, China issued a proposal for the construction of sponge cities across the entire region to significantly enhance the rainfall flood prevention and drainage capacity of cities and effectively improve the resilience of sponge city systems for flooding management. Therefore, this paper selected the Zhu pai-chong watershed in Nanning with frequent waterlogging disasters as an example. Based on underlying surface information, We used a coupled SWMM-LISFOOD model to simulate runoff and waterlogging processes and analyze the spatial and temporal evolution characteristics of the basin under 10 designed rainstorm return periods (0.25a-50a). The results confirm the substantial spatial and temporal variabilities of the runoff coefficient in the study area; impermeability was the main factor contributing to high runoff coefficient values. The spatial distribution characteristics of inundation area was general dispersion and local linear aggregation. Furthermore, this study assessed the effect of the control rate of blue‒green‒gray facilities on the actual storms, and the value ranged from only 48.6% (0.25a)-24.05% (50a). This study quantified the two-dimensional distribution of rainfall storage volume thresholds with or without considering the discharged from the pipe network. Quantitative mapping between the elements of "rainfall-storage volume of blue‒green‒gray facilities-runoff-drainage capacity of the pipe network-waterlogging level" was conducted within the study area as an example. Finally, an overall technical process scheme for rainfall and waterlogging management was proposed. The scheme covered the hydrological‒hydraulic mechanism, storage function of sponge facilities, engineering control response, nonengineering measures and intelligent management of rainfall and waterlogging during sponge city construction, which could provide critical scientific support for effective promotion of the construction of sponge cities in China.

A brief introduction of Xiangya's anti-epidemic history in a century. / ç™¾å¹´æ¹˜é› æŠ—ç–«ç®€å².

Based on archival materials, the Xiangya's anti-epidemic history in a century from its establishment to 2020 is divided into 4 stages. The first stage (1906-1926), Edward Hicks Hume and YAN Fuqing, the founders of Xiangya, prevented and controlled smallpox and plague. The second stage (1929-1953), during the resumption of Xiangya, students prevented and controlled cholera, plague, dysentery, typhus, and other infectious diseases. In the third stage (1953-1999), in a peacetime, Xiangya actively fought against schistosomiasis, hydatidosis, malaria, leprosy, tuberculosis and other epidemics. The fourth stage (2000-2020), the era of Central South University. Medical staff in Xiangya fight SARS, influenza A (H1N1) flu, Ebola hemorrhagic fever, coronavirus disease 2019, etc. Over the past hundred years, Xiangya people joined together to spread benevolence and love, apply medical knowledge and skills, combat the epidemic and rescue people in difficulties, which made a great contribution to the motherland and the people.