Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.

Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 ( = 647) and other respiratory viruses ( = 384). The model was trained with 811 CT scans, while 220 CT scans ( = 151 COVID-19; = 69 non-COVID-19) were used for independent validation.

Correlation between lung ultrasound and chest CT patterns with estimation of pulmonary burden in COVID-19 patients.

Purpose: The capability of lung ultrasound (LUS) to distinguish the different pulmonary patterns of COVID-19 and quantify the disease burden compared to chest CT is still unclear.

Methods: PCR-confirmed COVID-19 patients who underwent both LUS and chest CT at the Emergency Department were retrospectively analysed. In both modalities, twelve peripheral lung zones were identified and given a Severity Score basing on main lesion pattern.

Emergency CT for assessment and management of blunt traumatic splenic injuries at a Level 1 Trauma Center: 13-year study.

Purpose: To determine the relationship between multidetector computed tomography (MDCT) findings, management strategies, and ultimate clinical outcomes in patients with splenic injuries secondary to blunt trauma.

Materials And Methods: This Institutional Review Board-approved study collected 351 consecutive patients admitted at the Emergency Department (ED) of a Level I Trauma Center with blunt splenic trauma between October 2002 and November 2015. Their MDCT studies were retrospectively and independently reviewed by two radiologists to grade splenic injuries according to the American Association for the Surgery of Trauma (AAST) organ injury scale (OIS) and to detect intraparenchymal (type A) or extraparenchymal (type B) active bleeding and/or contained vascular injuries (CVI).

Detection of biliary and vascular anatomy in living liver donors: value of gadobenate dimeglumine enhanced MR and MDCT angiography.

Objective: To evaluate the performance of magnetic resonance (MR) and multidetector computed tomography (MDCT) in the assessment of living donor's vascular and biliary anatomy, having surgical findings as reference standard.

Methods: Thirty-two living liver donors underwent MR cholangiography (1.5-T; standard cholangiography pulse sequences and delayed acquisitions after administration of biliary contrast agent) for biliary anatomy evaluation.

Role of MDCT in the diagnosis of hepatocellular carcinoma in patients with cirrhosis undergoing orthotopic liver transplantation.

Objective: The purpose of this study was to assess the diagnostic performance of MDCT in the detection of hepatocellular carcinoma in patients with cirrhosis undergoing orthotopic liver transplantation.

Materials And Methods: Eighty-eight consecutively registered patients who underwent MDCT 6 months before liver transplantation were evaluated. The original reports were analyzed, and the CT images were retrospectively reevaluated independently by two radiologists who made the final interpretation in consensus.

A fully automated method for lung nodule detection from postero-anterior chest radiographs.

In the past decades, a great deal of research work has been devoted to the development of systems that could improve radiologists' accuracy in detecting lung nodules. Despite the great efforts, the problem is still open. In this paper, we present a fully automated system processing digital postero-anterior (PA) chest radiographs, that starts by producing an accurate segmentation of the lung field area.