Objectives: Ileocolic intussusception can be challenging to diagnose due to vague complaints, but rapid diagnosis and treatment can help prevent morbidity and mortality. Prior research has focused on radiologic ultrasound, with more recent studies focusing on point-of-care ultrasonography (POCUS). This systematic review and meta-analysis assesses the diagnostic accuracy of POCUS for children with suspected ileocolic intussusception.
Methods: PubMed, Embase, CINAHL, LILACS, the Cochrane databases, Google Scholar, conference abstracts, and bibliographies of selected articles were searched for studies evaluating the accuracy of POCUS for the diagnosis of intussusception in children. Data were dual extracted into a predefined worksheet, and quality analysis was performed with the QUADAS-2 tool. Data were summarized, and a meta-analysis was performed.
Results: Eleven studies (n = 2400 children) met our inclusion criteria. Overall, 14.4% of children had intussusception. POCUS was 95.1% (95% CI: 90.3% to 97.2%) sensitive and 98.1% (95% CI: 95.8% to 99.2%) specific with a positive likelihood ratio of 50 (95% CI: 23 to 113) and a negative likelihood ratio of 0.05 (95% CI: 0.03 to 0.09).
Conclusions: POCUS has excellent diagnostic accuracy for intussusception in children presenting to the emergency department.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.ajem.2022.06.025 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Transmantle pressure under the influence of free breathing: non-invasive quantification of the aqueduct pressure gradient in healthy adults.
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 PDFA machine learning-based model to predict POD24 in follicular lymphoma: a study by the Chinese workshop on follicular lymphoma.
Biomark 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).
Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology.
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 PDFAutomatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.
BMC 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).
Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images.
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.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!