Correlation of Chest X-Ray Scores in SARS-CoV-2 Patients With the Clinical Severity Classification and the Quick COVID-19 Severity Index.

Objectives This study aimed to assess the role of chest X-ray (CXR) scoring methods and their correlations with the clinical severity categories and the Quick COVID-19 Severity Index (qCSI). Methods We conducted a retrospective study of 159 COVID-19 patients who were diagnosed and treated at the University Medical Center between July and September 2021. Chest X-ray findings were evaluated, and severity scores were calculated using the modified CXR (mCXR), Radiographic Assessment of Lung Edema (RALE), and Brixia scoring systems.

Deep Learning Model With Convolutional Neural Network for Detecting and Segmenting Hepatocellular Carcinoma in CT: A Preliminary Study.

Introduction Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world. Early detection and accurate diagnosis of HCC play an important role in patient management. This study aimed to develop a convolutional neural network-based model to identify and segment HCC lesions utilizing dynamic contrast agent-enhanced computed tomography (CT).

Differentiation Between Surgical and Nonsurgical Intussusception: A Diagnostic Model Using Multi-Detector Computed Tomography.

Background: The differentiation of surgical from nonsurgical adult intussusception may enable the appropriate selection of management strategies.

Objective: This study aimed to investigate the diagnostic potential of multidetector computed tomography (MCDT) features to differentiate surgical from nonsurgical adult intussusception and develop a diagnostic model.

Methods: A retrospective study was performed on 96 patients with intussusceptions at the University Medical Center Hospital between January 2014 and January 2020.