Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - https://github.com/arnabkmondal/xViTCOS. The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8691725 | PMC |
http://dx.doi.org/10.1109/JTEHM.2021.3134096 | DOI Listing |
IJID Reg
March 2025
Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Shinjuku-ku, Japan.
Objectives: We examined shifts in labor productivity and their economic ramifications among adult patients with long COVID in Japan.
Methods: A total of 396 patients were categorized into three groups based on symptom progression: non-long COVID, long COVID recovered, and long COVID persistent. Patient-reported outcomes were assessed at three time intervals: 3, 6, and 12 months after COVID-19 diagnosis.
Front Cell Infect Microbiol
December 2024
Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
Objective: COVID-19 has evolved into a seasonal coronavirus disease, characterized by prolonged infection duration and repeated infections, significantly increasing the risk of patients developing long COVID. Our research focused on the immune responses in asymptomatic and mild cases, particularly the critical factors influencing serum immunoglobulin G (IgG) levels and their predictive value.
Methods: We conducted a retrospective analysis on data from 1939 asymptomatic or mildly symptomatic COVID-19 patients hospitalized between September 2022 and June 2023.
Cureus
November 2024
College of Osteopathic Medicine, Kansas City University of Medicine and Biosciences, Joplin, USA.
Background COVID-19 disease has caused a major global impact on health and mortality. This infection may predispose patients to thrombotic disease, caused by excessive inflammation, endothelial dysfunction, platelet activation, and stasis. In this study, we compared mortality rates in patients admitted to the hospital with the diagnosis of COVID-19, who also had the additional diagnosis of thrombosis with those who did not have thrombosis as an additional diagnosis.
View Article and Find Full Text PDFAdv Med Educ Pract
December 2024
Queen Charlotte's and Chelsea Hospital, Imperial College, London, UK.
Purpose: To determine the level of uptake of telemedicine among postgraduate obstetrics and gynaecology (O&G) trainees in London, and how they perceive its impact on their training.
Methods: A mixed-methods survey aimed at exploring trainee perspectives of telemedicine use in clinical practice and its implications for training. Study participants were O&G specialist doctors on the London (UK) training programme.
Front Med (Lausanne)
December 2024
Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hannover, Germany.
Rapid and sensitive diagnostic measures are a pre-requisite for the control of SARS-CoV-2 outbreaks. Dogs detect SARS-CoV-2-infected human individuals with high speed due to their extraordinary olfactory acuity. In the post-pandemic phase of SARS-CoV-2 it is difficult to obtain samples from infected humans for scent dog training.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!