Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection.

Purpose: This study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute uncomplicated Stanford type B aortic dissection (uTBAD) undergoing initial thoracic endovascular aortic repair (TEVAR).

Methods: We retrospectively evaluated 369 patients treated with TEVAR for acute uTBAD from January 2015 to December 2022. A three-dimensional (3D) deep convolutional neural network (CNN) automated radiomic feature extraction from CTA images.

Application of quantitative lung ultrasound instead of CT for monitoring COVID-19 pneumonia in pregnant women: a single-center retrospective study.

Background: Computed tomography (CT) is the preferred imaging technique for the evaluation of COVID-19 pneumonia. However, it is not suitable as a monitoring tool for pregnant women because of the risk of ionizing radiation damage to the fetus as well as the possible infection of others. In this study, we explored the value of bedside lung ultrasound (LUS) as an alternative to CT for the detection and monitoring of lung involvement in pregnant women with COVID-19.

Semiquantitative lung ultrasound scores in the evaluation and follow-up of critically ill patients with COVID-19: a single-center study.

Rationale And Objectives: Chest CT is not suitable for critically ill patients with COVID-19 and lung ultrasound (LUS) may play an important role for these patients. In this study, we summarized the findings of LUS and explore the value of semiquantitative LUS scores in evaluation and follow-up of COVID-19 pneumonia.

Materials And Methods: Retrospectively studied the LUS and chest CT imaging of 128 critically ill patients with COVID-19.

High Resolution CT Imaging Dynamic Follow-Up Study of Novel Coronavirus Pneumonia.

To explore the clinical characteristics and dynamic follow-up changes of high resolution CT (HRCT) in 270 patients with Coronavirus Disease 2019 (COVID-19) pneumonia. Two hundred seventy COVID-19 pneumonia patients were retrospectively analyzed, including 146 males and 124 females, with median age of 51 (9,89). The clinical features, laboratory examination indexes and HRCT evolution findings of 270 COVID-19 pneumonia patients were analyzed.

Correlation Between Diabetic Cognitive Impairment and Diabetic Retinopathy in Patients With T2DM by H-MRS.

To explore the correlation between diabetic cognitive impairment (DCI) and diabetic retinopathy (DR) through examining the cognitive function and the metabolism of the cerebrum in Type 2 diabetes mellitus (T2DM) by H-MRS. Fifty-three patients with T2DM were enrolled for this study. According to the fundus examination, the patients were divided into the DR group ( = 26) and the T2DM without DR group (T2DM group, = 27).

Quantitative Aortic Distensibility Measurement Using CT in Patients with Abdominal Aortic Aneurysm: Reproducibility and Clinical Relevance.

To investigate the reproducibility of aortic distensibility () measurement using CT and assess its clinical relevance in patients with infrarenal abdominal aortic aneurysm (AAA). 54 patients with infrarenal abdominal aortic aneurysm were studied to determine their distensibility by using 64-MDCT. Aortic cross-sectional area changes were determined at two positions of the aorta, immediately below the lowest renal artery (level 1.

Correlation between epicardial adipose tissue and severity of coronary artery stenosis evaluated by 64-MDCT.

Purpose: The purpose was to investigate the correlation between epicardial adipose tissue (EAT) thickness, EAT volume, and severity of coronary artery stenosis.

Methods: We retrospectively enrolled 188 patients that underwent coronary computed tomography (CT) angiography for clinically suspected coronary artery disease using 64-MDCT. Images were reconstructed using a retrospective electrocardiogram-gated algorithm with 0.