ArticleJournal of healthcare engineering2021
Chest CT Findings and Differential Diagnosis of
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 38 citations in OpenAlex.
- CT-based radiomics nomogram for distinguishingTranslational pediatrics · 2026Article
- Clinical utility of the targeted next-generation sequencing for detection of Mycoplasma pneumoniae and antimicrobial resistance genes for adults.BMC infectious diseases · 2026Article
- Clinical characteristics, predictive factors, and therapeutic outcomes of mycoplasma pneumoniae pneumonia with pleural effusion in children: a retrospective cohort study.Frontiers in pediatrics · 2026Article
- Immunopathogenesis, molecular phenotyping, and host-directed interventions for severe pediatricFrontiers in cellular and infection microbiology · 2026Review
- Development and validation of a machine learning model for predicting co-infection ofTranslational pediatrics · 2025Article
- Clinical value and radiographic features of low dose CT scans compared to X rays in diagnosing mycoplasma pneumonia in children.Scientific reports · 2025Article
- RETRACTED: Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning.PloS one · 2025Article
- Rapid detection ofFrontiers in cellular and infection microbiology · 2025Article
- Association betweenBiomedical reports · 2024Review
- Efficient lung cancer detection using computational intelligence and ensemble learning.PloS one · 2024Article
- A comprehensive review ofFrontiers in medicine · 2024Review
- Machine learning based model for detecting depression during Covid-19 crisis.Scientific African · 2023Article
- Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer.Computational and mathematical methods in medicine · 2022Article
- Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning.Computational intelligence and neuroscience · 2022Article
- Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor.BioMed research international · 2022Article
- Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image.BioMed research international · 2022Article
- PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer.BioMed research international · 2022Article
- Blockchain Framework for Secure COVID-19 Pandemic Data Handling and Protection.Computational intelligence and neuroscience · 2022Article
- Diagnosis and Prognostic Analysis ofContrast media & molecular imaging · 2022Article
- Patient Behavioral Analysis with Smart Healthcare and IoT.Behavioural neurology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In this day and age, 17% of children less than 5 years of age died of pneumonia; it is the common cause of children death. It is one of the main children respiratory infectious diseases, i.e., mycoplasma pneumonia (MP). The imaging examination can be adopted to quickly observe the morphology and scope of the pulmonary lesions and know the effect of disease treatment and subsequent changes in the disease in order to provide a basis for treatment. Therefore, the most commonly applied technology for detecting pneumonia in children is imaging technology, including chest X-ray and CT. Objectives: The main objective of the work is to investigate the chest computed tomography (CT) findings of children patients with Methods: There were comparisons on the incidence of bronchial and pulmonary interstitial lesions, the degree of lymph node enlargement, the volume and depth of pleural effusion, and the location and morphology of the pulmonary lesions in the chest CT images of children patients from the two groups. All the experiments are done in the MATLAB. Results: The results showed that the proportions of reticular shadow, ground glass shadow, bronchial inflation phase, tube wall thickening, and vascular bundle thickening on the CT images of children patients from the MPP group were dramatically higher than those of the MP + SP group ( Conclusion: In conclusion, the chest CT images of children patients from the MPP group were mainly pulmonary interstitial changes. Furthermore, the alveolar inflammation could be observed on the CT images shown when children patients were combined with SP infection. The more obvious manifestations were that the flaky shadows appeared in the lungs, the pleural effusion became thicker, and the transverse diameters of enlarged lymph nodes were bigger.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.