Background: At present, the diagnosis of COVID-19 depends on real-time reverse transcriptase polymerase chain reaction (RT-PCT). On imaging, computed tomography (CT) manifestations resemble those seen in viral pneumonias, with multifocal ground-glass opacities and consolidation in a peripheral distribution being the most common findings. Although these findings lack specificity for COVID-19 diagnosis on imaging grounds, CT could be used to provide objective assessment about the extension of the lung opacities, which could be used as an imaging surrogate for disease burden. Chest CT scan may be helpful in early diagnosing of COVID-19.
Objective: The current study investigated the diagnostic accuracy and false-positive rate of chest CT in detecting COVID-19 pneumoniain a population with clinical suspicion using RT-PCR testing as reference standard.
Materials And Methods: In this prospective single centerstudy performed on 612 cases with clinical suspicion of COVID-19, all adult symptomatic ED patients had both a CT scan and a PCR upon arrival at the hospital. CT results were compared with PCR test (s) and diagnostic accuracy was calculated.
Results: Between February 15, 2020 to July 15, 2020, 612 symptomatic ED patients were included. In total, 78.5% of patients had a positive PCR and 82.8% a positive CT, resulting in a sensitivity of 94.2%, specificity 76.4%, likelihood ratio (LR) + 2.94 and (LR) - 0.18. The PPV was 76.7% and NPV 94.1%. The sensitivity of the CT tended to be higher (100.0%) in those with severe risk pneumonia than in patients with low/medium risk pneumonia (90.3%, = 0.42). In patients with sepsis, sensitivity was significantly higher than in those without sepsis (99.5 vs. 63.5%, < 0.001). The diagnostic ability of chest CT was found to be rather high with 92.1%, concordance rate between findings of CT and PCR. In 48 (7.8%) patients discordant findings between CT and PCR were observed. The positive predictive values (PPV) and accuracy of chest CT in diagnosing COVID-19 were higher in patients ≥60 years than that in patients <60 years ( = 0.001 and 0.004, respectively). The specificity and NPV of chest CT in diagnosing COVID-19 were greater for women than that for men ( = 0.007 and 0.03, respectively); and no difference existed for sensitivity, PPV and accuracy ( = 0.43, 0.69 and 0.31, respectively). In most cases, the CT scan was considered suspicious for COVID-19, while the PCR was negative (37/48, 70.8%). In the majority of these, the diagnosis at discharge was pulmonary infection ( = 26; 74.3%). The current study included repeated PCRs and explored discordant test results, which showed that in about 45.9% of patients with false-positive CT scans, other viral pathogens were detected. The false-positive rate of CT findings in the diagnosis of COVID-19 pneumonia was 7.2%.
Conclusion: High diagnostic accuracy of chest CT findings with typical and relatively atypical CT manifestations of COVID-19 leads to a low rate of missed diagnosis. Normal chest CT can be found in RT-PCR positive COVID-19 cases, and typical CT manifestations can be found in RT-PCR negative cases. Therefore, a combination of both CT and RT-PCR for future follow-up, management and medical surveillance is recommended considering the false-positive results of chest CT in the diagnosis of COVID-19 pneumonia.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7996710 | PMC |
http://dx.doi.org/10.4103/ijri.IJRI_377_20 | DOI Listing |
ACS Sens
January 2025
Department of Physics and Astronomy, Franklin College of Arts and Sciences, The University of Georgia, Athens, Georgia 30602, United States.
Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.
View Article and Find Full Text PDFJMIR Cancer
January 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
View Article and Find Full Text PDFReprod Fertil Dev
January 2025
Fertility & Research Centre, Discipline of Women health, School of Clinical Medicine and the Royal Hospital for Women, University of New South Wales, Sydney, NSW, Australia.
Pre-implantation genetic testing for aneuploidy (PGT-A) via embryo biopsy helps in embryo selection by assessing embryo ploidy. However, clinical practice needs to consider the invasive nature of embryo biopsy, potential mosaicism, and inaccurate representation of the entire embryo. This creates a significant clinical need for improved diagnostic practices that do not harm embryos or raise treatment costs.
View Article and Find Full Text PDFJ Am Podiatr Med Assoc
January 2025
†Medical Point Gaziantep Hospital, Gaziantep, Turkey.
Background: The incidence of diabetic foot infections is increasing due to the rising number of persons with diabetes and the prolonged life expectancy. It is vital to differentiate soft-tissue infection (STI) from diabetic foot osteomyelitis (DFO), as treatment modalities and durations vary widely, but this can be challenging. We aimed to assess the blood concentration levels of the high mobility group box 1 protein (HMGB-1) in STI and DFO compared to healthy subjects, and to investigate whether this protein could contribute to differentiating STI from DFO.
View Article and Find Full Text PDFLymphology
January 2025
Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Lymphadenopathy is associated with lymph node abnormal size or consistency due to many causes. We employed the deep convolutional neural network ResNet-34 to detect and classify CT images from patients with abdominal lymphadenopathy and healthy controls. We created a single database containing 1400 source CT images for patients with abdominal lymphadenopathy (n = 700) and healthy controls (n = 700).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!