ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2015
Severity quantification of pediatric viral respiratory illnesses in chest X-ray images.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Pediatric lung imaging features of COVID-19: A systematic review and meta-analysis.Pediatric pulmonology · 2021Pooled it
- Chest X-ray lung imaging features in pediatric COVID-19 and comparison with viral lower respiratory infections in young children.Pediatric pulmonology · 2021Article
- Phenotypical Sub-setting of the First Episode of Severe Viral Respiratory Infection Based on Clinical Assessment and Underlying Airway Disease: A Pilot Study.Frontiers in pediatrics · 2020Article
- Automatic tissue characterization of air trapping in chest radiographs using deep neural networks.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
Accurate assessment of severity of viral respiratory illnesses (VRIs) allows early interventions to prevent morbidity and mortality in young children. This paper proposes a novel imaging biomarker framework with chest X-ray image for assessing VRI's severity in infants, developed specifically to meet the distinct challenges for pediatric population. The proposed framework integrates three novel technical contributions: a) lung segmentation using weighted partitioned active shape model, b) obtrusive object removal using graph cut segmentation with asymmetry constraint, and c) severity quantification using information-theoretic heterogeneity measures. This paper presents our pilot experimental results with a dataset of 148 images and the ground-truth severity scores given by a board-certified pediatric pulmonologist, demonstrating the effectiveness and clinical relevance of the presented framework.
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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.