Background: Lateral flow tests (LFT) are point-of-care rapid antigen tests that allow isolation and control of disease outbreaks through convenient, practical testing. However, studies have shown significant variation in their diagnostic accuracy. We conducted a systematic review of the diagnostic accuracy of LFTs for the detection of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) to identify potential factors affecting their performance.
Methods: A systematic search of online databases was carried out to identify studies assessing the sensitivity and specificity of LFTs compared with polymerase chain reaction (PCR) tests. Data were extracted and used to calculate pooled sensitivity and specificity. Meta-regression analysis was conducted to identify covariates influencing diagnostic accuracy.
Results: In total, 76 articles with 108,820 test results were identified for analysis. Pooled sensitivity and specificity were 72% (95% confidence interval (CI): 0.68-0.76) and 100% (95% CI: 0.99-1.00), respectively. Staff operation of the LFT showed a statistically significant increase in sensitivity (p=0.04) and specificity (p=0.001) compared with self-operation by the test subjects. The use of LFTs in symptomatic patient subgroups also resulted in higher test sensitivity.
Conclusion: LFTs display good sensitivity and extremely good specificity for SARS-CoV-2 antigen detection; they become more sensitive in patients with symptoms and when performed by trained professionals.
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http://dx.doi.org/10.7861/clinmed.2022-0319 | DOI Listing |
Fluids Barriers CNS
January 2025
Medical Image Processing Department, CHU Amiens-Picardie University Hospital, Amiens, France.
Background: The pressure gradient between the ventricles and the subarachnoid space (transmantle pressure) is crucial for understanding CSF circulation and the pathogenesis of certain neurodegenerative diseases. This pressure can be approximated by the pressure difference across the aqueduct (ΔP). Currently, no dedicated platform exists for quantifying ΔP, and no research has been conducted on the impact of breathing on ΔP.
View Article and Find Full Text PDFBiomark Res
January 2025
Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Background: Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.
Methods: A cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio).
BMC Cancer
January 2025
Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
Objective: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer. However, in China, limited familiarity with the Diff-Quik staining method and a shortage of trained cytopathologists hamper utilization of ROSE. Therefore, developing an improved deep learning model to assist clinicians in promptly and accurately evaluating Diff-Quik stained cytology samples during ROSE has important clinical value.
View Article and Find Full Text PDFBMC Neurol
January 2025
Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, School of Medicine, College of Medicine, National Sun Yat-Sen University, No. 123 Ta-Pei Road, Niao-Sung Dist, Kaohsiung, 83305, Taiwan.
Background And Purpose: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a 3D convolutional neural network and a 3D Transformer-based model for white matter hyperintensities segmentation, focusing on their efficacy with limited datasets and similar computational resources.
Materials And Methods: We implemented a convolution-based model (3D ResNet-50 U-Net with spatial and channel squeeze & excitation) and a Transformer-based model (3D Swin Transformer with a convolutional stem).
Sci Rep
January 2025
College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China.
Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-cystic, polycystic, internal capsule collapse, solid mass, and calcified subtypes.
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