Purpose: To improve the risk stratification of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an experimental chest X-ray (CXR) scoring system for quantifying lung abnormalities was introduced in our Diagnostic Imaging Department. The purpose of this study was to retrospectively evaluate correlations between the CXR score and the age or sex of Italian patients infected with SARS-CoV-2.
Materials And Methods: Between March 4, 2020, and March 18, 2020, all CXR reports containing the new scoring system were retrieved. Only hospitalized patients with SARS-CoV-2 infection were enrolled. For each patient, age, sex, and the CXR report containing the highest score were considered for the analysis. Patients were also divided into seven groups according to age. Nonparametric statistical tests were used to examine the relationship between the severity of lung disease and the age or sex.
Results: 783 Italian patients (532 males and 251 females) with SARS-CoV-2 infection were enrolled. The CXR score was significantly higher in males than in females only in groups aged 50 to 79 years. A significant correlation was observed between the CXR score and age in both males and females. Males aged 50 years or older and females aged 80 years or older with coronavirus disease 2019 showed the highest CXR score (median ≥ 8).
Conclusions: Males aged 50 years or older and females aged 80 years or older showed the highest risk of developing severe lung disease. Our results may help to identify the highest-risk patients and those who require specific treatment strategies.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7194500 | PMC |
http://dx.doi.org/10.1007/s11547-020-01202-1 | DOI Listing |
J Imaging Inform Med
January 2025
Leiden University Medical Center (LUMC), Leiden, the Netherlands.
Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia.
Objective: We aimed to develop a highly interpretable and effective, machine-learning based risk prediction algorithm to predict in-hospital mortality, intubation and adverse cardiovascular events in patients hospitalised with COVID-19 in Australia (AUS-COVID Score).
Materials And Methods: This prospective study across 21 hospitals included 1714 consecutive patients aged ≥ 18 in their index hospitalization with COVID-19. The dataset was separated into training (80%) and test sets (20%).
J Imaging
January 2025
Department of Food Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Pneumonia, a leading cause of mortality in children under five, is usually diagnosed through chest X-ray (CXR) images due to its efficiency and cost-effectiveness. However, the shortage of radiologists in the Least Developed Countries (LDCs) emphasizes the need for automated pneumonia diagnostic systems. This article presents a Deep Learning model, Zero-Order Optimized Convolutional Neural Network (ZooCNN), a Zero-Order Optimization (Zoo)-based CNN model for classifying CXR images into three classes, Normal Lungs (NL), Bacterial Pneumonia (BP), and Viral Pneumonia (VP); this model utilizes the Adaptive Synthetic Sampling (ADASYN) approach to ensure class balance in the Kaggle CXR Images (Pneumonia) dataset.
View Article and Find Full Text PDFNEJM AI
October 2024
Google, Mountain View, CA, USA.
Background: Using artificial intelligence (AI) to interpret chest X-rays (CXRs) could support accessible triage tests for active pulmonary tuberculosis (TB) in resource-constrained settings.
Methods: The performance of two cloud-based CXR AI systems - one to detect TB and the other to detect CXR abnormalities - in a population with a high TB and human immunodeficiency virus (HIV) burden was evaluated. We recruited 1978 adults who had TB symptoms, were close contacts of known TB patients, or were newly diagnosed with HIV at three clinical sites.
Eur Radiol
January 2025
Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists.
Materials And Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLaVA network.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!