Objective:To investigate the impact of artificial intelligence imaging optimization technique on the image quality and radiation dose of low-dose chest CT scan.Methods:Eighty patients who underwent chest CT examination in the Jilin University 1st hospital from July to August, 2019 were randomly divided into two groups(A, B), with 40 patients in each. The voltage of group A was 100 kV, while the other was 120 kV. According to different reconstruction method , group A was divided into two subgroups, group A1 and group A2. The images of A1 were reconstructed by iterative algorithm (ClearView 50%), while A2 images were optimized A1 by NeuAI imaging optimization technique. Group B used iterative algorithm (ClearView 50%) to reconstruct the image. The CT dose index (CTDI vol), dose-length product (DLP) and effective radiation dose ( E) of group A and group B were recorded and compared.Objective the evaluation indicators were CT noise (SD), signal-to-noise ratio (SNR) and comparative noise ratio (CNR) of ROI. Subjective evaluation was done by 2 chief radiologists using double-blind method and image quality was graded by 5-point Likert scale. Results:The patient characteristics between group A and group B showed no significant differences( P>0.05). Compared with group B, the effective radiation dose in group A was reduced by 72.1% [(1.48±0.49) mSv vs. (5.30±1.40) mSv]. The SD in group A1 was higher than that in group B, while SNR and CNR were lower ( ZSD=-4.24, ZSNR=-2.54, tCNR=-2.27, P<0.05). The SD in group A2 was significantly lower than that in group B ( ZSD=-28.24, P<0.001), and SNR and CNR were significantly higher than that in group B ( tSNR=-26.04, tCNR=-36.88, P<0.001). There was no significant difference in subjective scores of image noise between group A2 and group B, while subjective scores of lung structure in group B were better than those in group A2( χ2=4.96、7.04, P<0.05). Conclusions:Although the radiation dose was reduced by 72.1%, the low-dose chest CT images optimized by AI could reach the image quality level of standard dose.

Objective:To investigate the predictive value of clinical factor model, deep learning model based on baseline plain CT images, and combination of both for predicting hematoma expansion in cerebral hemorrhage.Methods:The study was cross-sectional. Totally 471 cerebral hemorrhage patients who were firstly diagnosed in the Second Affiliated Hospital of Soochow University from January 2017 to December 2021 were collected retrospectively. These patients were randomly divided into a training dataset ( n=330) and a validation dataset ( n=141) at a ratio of 7∶3 by using the random function. All patients underwent two noncontrast CT examinations within 24 h and an increase in hematoma volume of >33% or an absolute increase in hematoma volume of >6 ml was considered hematoma enlargement. According to the presence or absence of hematoma enlargement, all patients were divided into hematoma enlargement group and hematoma non-enlargement group.Two-sample t test, Mann-Whitney U test or χ2 test were used for univariate analysis. The factors with statistically significant differences were included in multivariate logistic regression analysis, and independent influences related to hematoma enlargement were screened out to establish a clinical factor model. ITK-SNAP software was applied to manually label and segment the cerebral hemorrhage lesions on plain CT images to train and build a deep learning model based on ResNet50 architecture. A combination model for predicting hematoma expansion in cerebral hemorrhage was established by combining independent clinical influences with deep learning scores. The value of the clinical factor model, the deep learning model, and the combination model for predicting hematoma expansion in cerebral hemorrhage was evaluated using receiver operating characteristic (ROC) curves and decision curves in the training and validation datasets. Results:Among 471 cerebral hemorrhage patients, 136 cases were in the hematoma enlargement group and 335 cases were in the hematoma non-enlargement group. Regression analyses showed that male ( OR=1.790, 95% CI 1.136-2.819, P=0.012), time of occurrence ( OR=0.812, 95% CI 0.702-0.939, P=0.005), history of oral anticoagulants ( OR=2.157, 95% CI 1.100-4.229, P=0.025), admission Glasgow Coma Scale score ( OR=0.866, 95% CI 0.807-0.929, P<0.001) and red blood cell distribution width ( OR=1.045, 95% CI 1.010-1.081, P=0.011) were the independent factors for predicting hematoma expansion in cerebral hemorrhage. ROC curve analysis showed that in the training dataset, the area under the curve (AUC) of clinical factor model, deep learning model and combination model were 0.688 (95% CI 0.635-0.738), 0.695 (95% CI 0.642-0.744) and 0.747 (95% CI 0.697-0.793) respectively. The AUC of the combination model was better than that of the clinical model ( Z=0.54, P=0.011) and the deep learning model ( Z=2.44, P=0.015). In the validation dataset, the AUC of clinical factor model, deep learning model and combination model were 0.687 (95% CI 0.604-0.763), 0.683 (95% CI 0.599-0.759) and 0.736 (95% CI 0.655-0.806) respectively, with no statistical significance. Decision curves showed that the combination model had the highest net benefit rate and strong clinical practicability. Conclusions:Both the deep learning model and the clinical factor model established in this study have some predictive value for hematoma expansion in cerebral hemorrhage; the combination model established by the two together has the highest predictive value and can be applied to predict hematoma expansion.

ObjectiveTo observe the clinical efficacy of acupuncture combined with bloodletting and cupping in the treatment of chronic spontaneous urticaria（CSU） and to explore its potential mechanisms of action. MethodsSeventy CSU patients were randomly divided into loratadine group and acupuncture + bloodletting group， with 35 patients in each group. The loratadine group received oral loratadine tablets， 10 mg once daily in the evening. The acupuncture + bloodletting group received acupuncture at Zhongwan （CV 12）， Guanyuan （CV 4）， Tianshu （ST 25）， Zusanli （ST 36）， Sanyinjiao （SP 6）， Xuehai （SP 10）， Quchi （LI 11）， Hegu （LI 4）， Taichong （LR 3）， Baihui （GV 20）， and Shenting （GV 24）， once daily，along with bloodletting and cupping at Dazhui （GV 14） and Geshu （BL 17）， every other day. Both groups were treated for 4 weeks. The 7-day urticaria activity score（UAS7） was assessed before and after the treatment， and levels of serum immunoglobulin E （IgE）， interleukin-4 （IL-4）， interleukin-5 （IL-5）， eosinophil cationic protein （ECP）， plasma tissue factor （TF）， activated factor Ⅶ （FⅦa）， prothrombin fragment 1+2 （F1+2）， D-dimer （D-D） and complement component 5a （C5a） were detected. ResultsA total of 65 patients were included in the final analysis， 32 in the loratadine group and 33 in the acupuncture + bloodletting group. Before treatment， there was no significant difference in UAS7 score， serum IgE， IL-4， IL-5， ECP levels， or plasma TF， FⅦa， F1+2， D-D， C5a levels between groups （P＞ 0.05）. After treatment， both groups showed significant reductions in UAS7 score， serum IgE， IL-4， IL-5， and plasma TF， FⅦa， F1+2， D-D， and C5a levels compared to those before treatment （P＜0.01）. However， after treatment， there was no significant difference in UAS7 score and serum ECP， IgE， IL-4， IL-5 levels between groups （P＞0.05）. The acupuncture + bloodletting group showed lower plasma TF， FⅦa， F1+2， D-D and C5a levels compared to the loratadine group （P＜0.05 or P＜0.01）. ConclusionAcupuncture combined with bloodletting and cupping can effectively improve the skin symptoms of CSU patients and reduce the levels of inflammatory factors. The potential mechanism of action may involve the regulation of the coagulation-complement-mast cell activation axis， thereby inhibiting mast cell degranulation.