Objective: To investigate the prevalence and influencing factors of suicidal ideation and the effects of daily stress and positive mental health on suicidal ideation among college freshmen in Wuhan. This study aims to provide a scientific basis for mental health improvement and suicide prevention among college students from the perspective of positive psychology. Methods: In December 2020, a convenient sampling method was used to select 2 592 freshmen from three universities in Wuhan. Demographic information, suicidal ideation, positive mental health, and daily stress were assessed. T test and Logistic regression analysis were used to analyze the status and influencing factors of suicidal ideation among college freshmen. Results: The one year prevalence of suicidal ideation was 27.5%. Freshmen with suicidal ideation had higher daily stress scores and daily stress scores in different domains than freshmen without suicidal ideation( t =-13.00--4.68)， the positive mental health scores of freshmen with suicidal ideation were lower than freshmen without suicidal ideation ( t =17.14， P <0.01). Female ( OR=1.72, 95%CI =1.44-2.05), the higher education level of the mother (OR=1.27, 95%CI=1.05-1.53) and total experience of daily stress ( OR=1.11, 95%CI =1.09- 1.13 ) were associated with risk of oneyear suicidal ideation. Positive mental health was negatively associated with suicidal ideation ( OR=0.88, 95%CI=0.87-0.90, P <0.01) and had a moderating effect on the association between daily stress and suicidal ideation. Conclusion Suicidal ideation among college freshmen is closely related to daily stress and positive mental health. It is necessary to pay close attention to daily stress of freshmen and reduce suicidal ideation by improving positive mental health.

A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed.This model mainly included key points detection and Cobb angle measurement.748 full-length standing spinal X-ray images were retrospectively collected,of which 602 images were used to train and validate the model,and 146 images were used to test the model performance.The results showed that the model had good diagnostic and classification performance,with an accuracy of 94.5%.Compared with experts'measurement,94.9%of its Cobb angle measurement results were within the clinically acceptable range.The average absolute difference was 2.1°,and the consistency was also excellent(r2≥0.9552,P<0.001).In the future,this model could be applied clinically to improve doctors'diagnostic efficiency.

Objective A deep learning-based method for evaluating the quality of pediatric pelvic X-ray images is proposed to construct a diagnostic model and verify its clinical feasibility.Methods Three thousand two hundred and forty-seven children with anteroposteric pelvic radiographs are retrospectively collected and randomly divided into training datasets,validation datasets and test datasets.Artificial intelligence model is conducted to evaluate the reliability of quality control model.Results The diagnostic accuracy,area under ROC curve,sensitivity and specificity of the model are 99.4%,0.993,98.6%and 100.0%,respectively.The 95%consistency limit of the pelvic tilt index of the model is-0.052-0.072.The 95%consistency threshold of pelvic rotation index is-0.088-0.055.Conclusion This is the first attempt to apply AI algorithm to the quality assessment of children's pelvic radiographs,and has significantly improved the diagnosis and treatment status of DDH in children.