Objective To propose a lung artery segmentation method that integrates shape and position prior knowledge, aiming to solve the issues of inaccurate segmentation caused by the high similarity and small size differences between the lung arteries and surrounding tissues in CT images. Methods Based on the three-dimensional U-Net network architecture and relying on the PARSE 2022 database image data, shape and position prior knowledge was introduced to design feature extraction and fusion strategies to enhance the ability of lung artery segmentation. The data of the patients were divided into three groups: a training set, a validation set, and a test set. The performance metrics for evaluating the model included Dice Similarity Coefficient (DSC), sensitivity, accuracy, and Hausdorff distance (HD95). Results The study included lung artery imaging data from 203 patients, including 100 patients in the training set, 30 patients in the validation set, and 73 patients in the test set. Through the backbone network, a rough segmentation of the lung arteries was performed to obtain a complete vascular structure; the branch network integrating shape and position information was used to extract features of small pulmonary arteries, reducing interference from the pulmonary artery trunk and left and right pulmonary arteries. Experimental results showed that the segmentation model based on shape and position prior knowledge had a higher DSC (82.81%±3.20% vs. 80.47%±3.17% vs. 80.36%±3.43%), sensitivity (85.30%±8.04% vs. 80.95%±6.89% vs. 82.82%±7.29%), and accuracy (81.63%±7.53% vs. 81.19%±8.35% vs. 79.36%±8.98%) compared to traditional three-dimensional U-Net and V-Net methods. HD95 could reach (9.52±4.29) mm, which was 6.05 mm shorter than traditional methods, showing excellent performance in segmentation boundaries. Conclusion The lung artery segmentation method based on shape and position prior knowledge can achieve precise segmentation of lung artery vessels and has potential application value in tasks such as bronchoscopy or percutaneous puncture surgery navigation.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Over the past three decades， China has made significant progress in the prevention and control of viral hepatitis， and the incidence rates of new-onset pediatric hepatitis B virus infections and acute viral hepatitis in the population have reduced to a relatively low level； however， there is still a heavy disease burden of chronic viral hepatitis in China， which severely affects the health status of the population. This study systematically summarizes the achievements of viral hepatitis prevention and control in China， analyzes existing problems and challenges， and proposes comprehensive prevention and control strategies and measures to eliminate viral hepatitis as a public health threat based on the national conditions of China， in order to provide a reference for related departments in China on how to achieve the action targets for eliminating viral hepatitis as a public health threat by 2030.

AIM:To analyze the association between mobile phone addiction and high myopia among college students.METHODS:We conducted a cross-sectional questionnaire survey in December 2022 on all students of a university in Shaanxi Province, and the questionnaire included socio-demographic characteristics, mobile phone addiction, high myopia, and lifestyle. Binary Logistic regression model was used to analyze the association between mobile phone addiction and high myopia among college students.RESULTS:A total of 19 952 college students were included. The prevalence of high myopia was 7.31%. The rate of mobile phone addiction was 25.68%, and the mobile phone addiction score was 37.59±13.38. The incidence of high myopia among college students with mobile phone addiction was higher than non-mobile phone addiction(P<0.001). After adjusting for socio-demographic characteristics and lifestyle, the risk of high myopia among college students with mobile phone addiction was 1.274 times(95%CI:1.131-1.434)higher than non-mobile phone addiction. For each point increase of total mobile phone addiction score, withdrawal symptoms score, salience score, social comfort score, and mood changes score, the risk of high myopia among college students increased by 0.9%(95%CI:1.005-1.013), 2.0%(95%CI:1.010-1.030), 2.6%(95%CI:1.010-1.043), 4.8%(95%CI:1.030-1.066), and 3.3%(95%CI:1.014-1.052), respectively.CONCLUSION:Mobile phone addiction is significantly associated with the increased risk of high myopia among college students, and early intervention of mobile phone use may reduce the risk of high myopia among college students.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To investigate the value of artificial intelligence-assisted compressed sensing (ACS) technology for intravenous gadolinium contrast-enhanced magnetic resonance imaging of the inner ear using three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) sequence. Methods The patients received gadolinium contrast-enhanced magnetic resonance imaging using ACS and united compressed sensing (uCS) 3D-FLAIR at Zhongshan Hospital, Fudan University from January to November 2024 were prospectively enrolled. The repetition time was 16 000 ms, and acquisition time was 6 min 40 s and 10 min 24 s in ACS 3D-FLAIR and uCS 3D-FLAIR, respectively. The images on the two sequences were evaluated independently by two radiologists. The image quality of the two sequences was subjectively evaluated and compared. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between the two sequences. The grading consistencies using two sequences and between the two doctors were analyzed. Results There was no statistically difference in subjective score of image quality between the two sequences. SNR and CNR of the ACS 3D-FLAIR sequence were significantly higher than those of the uCS 3D-FLAIR sequence (P＜0.001). The kappa values of grades of cochlear and vestibular endolymphatic hydrops were 0.942 and 0.888 using two sequences (P＜0.001). The kappa values of grades of cochlear and vestibular endolymphatic hydrops using the ACS 3D-FLAIR sequence between the two doctors were 0.784 and 0.831, respectively (P＜0.001); the kappa values of grades of cochlear and vestibular endolymphatic hydrops using uCS 3D-FLAIR sequence between the two doctors were 0.725 and 0.756, respectively (P＜0.001). Conclusions ACS 3D-FLAIR could provide higher SNR and CNR than uCS 3D-FLAIR, and is more suitable for intravenous gadolinium contrast-enhanced magnetic resonance imaging of the inner ear; the endolymphatic hydrops grades using ACS 3D-FLAIR is similar to use uCS 3D-FLAIR.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.