Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

OBJECTIVE To compare the ability of ultra-high-resolution CT(U-HRCT)and multi-slice spiral CT(MSCT)to display key vocal transmission structures in the middle ear.METHODS Subjects with normal middle ear structures who underwent 0.1 mm layer thickness U-HRCT and 0.625 mm layer thickness MSCT scans at the same time in Beijing Friendship Hospital affiliated to Capital Medical University from December 2019 to August 2024 were retrospectively enrolled.Two experienced head and neck radiologists reconstruct standard transsectional,coronal images based on the thinnest layer thickness.According to the 5-point method,16 key sound transmission structures of the middle ear,including malleus,incus and stapes,as well as joints,ligaments and tendons,were evaluated for image quality scoring.The standard deviation(SD)value,signal noise ratio(SNR),and contrast noise ratio(CNR)of bone in the malleus region and intratympanic gas were measured and calculated on the two examination images.RESULTS Thirty patients(47 sides)with normal middle ear structure were included,including 18 males and 12 females.The two physicians compared the results of U-HRCT in showing malleus head,malleus neck,malleus handle,incus body,long process,and short process,5 points accounted for 100%,and the 5-point scores of incudomalleolar joint space,incudostapedial joint space,stapes footplate and annular ligament were 100%,98.29%,75.83%and 77.83%,respectively,which were significantly higher than those of MSCT(P<0.001).In addition,U-HRCT showed higher scores for lenticular process,stapes head,anterior arch of stapes,posterior arch of stapes,annular ligament,stapes muscle,and tendo musculi tensoris tympani than MSCT(P<0.001),and the lenticular process showed a 100%display rate.There was no significant difference in the SNR between the two groups(P>0.05),but the SD value of the malleus in U-HRCT was 161.6±36.4,which was significantly lower than that in MSCT(297.8±128.1),and the difference was statistically significant(P<0.001).CONCLUSION U-HRCT can clearly visualize the key sound transmission structures of the middle ear,and its visualization ability is significantly better than that of MSCT.

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver. MRI has become an important imaging method for non-invasive diagnosis and evaluation of HCC in clinics because of its advantageous aspects, such as its non-radiative nature, superior detection, and qualitative accuracy over CT and ultrasound. Various MRI techniques, including hepatobiliary-specific contrast agents, magnetic resonance elastography, diffusion-weighted imaging, and others, can diagnose HCC or evaluate its malignant biological behavior from different dimensions such as blood supply, cell function, tissue hardness, and water molecule diffusion. This article introduces the current status and prospects of various MRI techniques for HCC diagnosis and evaluation.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective To develop and validate a deep learning model for automatic identification of liver CT contrast-enhanced phases.Methods A total of 766 patients with liver CT contrast-enhanced images were retrospectively collected.A three-phase classification model and an arterial phase(AP)classification model were developed,so as to automatically identify liver CT contrast-enhanced phases as early arterial phase(EAP)or late arterial phase(LAP),portal venous phase(PVP),and equilibrium phase(EP).In addition,221 patients with liver CT contrast-enhanced images in 5 different hospitals were used for external validation.The annotation results of radiologists were used as a reference standard to evaluate the model performances.Results In the external validation datasets,the accuracy in identifying each enhanced phase reached to 90.50%-99.70%.Conclusion The automatic identification model of liver CT contrast-enhanced phases based on residual network may provide an efficient,objective,and unified image quality control tool.