Clinical prediction scale approach derived from a retrospective study to reduce the number of urgent, low-value cranial CT scans.

Purpose: Fifty percent of cranial CT scans performed achieve no benefit and entail risks. Our aim is to determine the yield of non-traumatic urgent cranial-CT and develop a pretest clinical probability scale approach.

Methods: Adult patients seen in our emergency department between 2017-2021 and referred for urgent cranial-CT for non-traumatic reasons were retrospectively recruited and randomly selected.

Inappropriate requests for cranial CT scans in emergency departments increase overuse and reduce test performance.

Purpose: The number of non-traumatic urgent cranial computed tomography (NT-UCCT) is exponentially increasing but limited research has been conducted on the quality of clinical justification. Accordingly, we aimed (1) to assess how clinical information in the electronic NT-UCCT request agreed with that provided in the patient's emergency department discharge summary and (2) to analyze the potential effect of those discrepancies on the NT-UCCT overload.

Material And Methods: Patients undergoing NT-UCCT in 2017-2021 were randomly selected for this retrospective research-board-approved study.

Digital tomosynthesis improves chest radiograph accuracy and reduces microbiological false negatives in COVID-19 diagnosis.

Purpose: Diagnosing pneumonia by radiograph is improvable. We aimed (a) to compare radiograph and digital thoracic tomosynthesis (DTT) performances and agreement for COVID-19 pneumonia diagnosis, and (b) to assess the DTT ability for COVID-19 diagnosis when polymerase chain reaction (PCR) and radiograph are negative.

Methods: Two emergency radiologists with 11 (ER1) and 14 experience-years (ER2) retrospectively evaluated radiograph and DTT images acquired simultaneously in consecutively clinically suspected COVID-19 pneumonia patients in March 2020-January 2021.

[Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia].

Objective: Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays.

A comparison of Covid-19 early detection between convolutional neural networks and radiologists.

Background: The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists.

Early radiological worsening of SARS-CoV-2 pneumonia predicts the need for ventilatory support.

Objectives: Identifying early markers of poor prognosis of coronavirus disease 2019 (COVID-19) is mandatory. Our purpose is to analyze by chest radiography if rapid worsening of COVID-19 pneumonia in the initial days has predictive value for ventilatory support (VS) need.

Methods: Ambispective observational ethically approved study in COVID-19 pneumonia inpatients, validated in a second outpatient sample.

Prognostic value of computed tomography pulmonary angiography indices in patients with cancer-related pulmonary embolism: Data from a multicenter cohort study.

Objective: To analyze the prognostic value of pulmonary artery obstruction versus right-ventricle (RV) dysfunction radiologic indices in cancer-related pulmonary embolism (PE).

Methods: We enrolled 303 consecutive patients with paraneoplastic PE, evaluated by computed tomography pulmonary angiography (CTPA) between 2013 and 2014. The primary outcome measure was serious complications at 15days.