ReviewAnnals of translational medicine2017
Lung imaging: how to get better look inside the lung.
Review in Annals of translational medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04989439 (Comparison of Computertomography Scan, Electrical Impedance Tomography, and Ultrasound of the Lung in Infants - A Prospective Explorative Observational Study), which is not on this map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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.
Comparison of Computertomography Scan, Electrical Impedance Tomography, and Ultrasound of the Lung in Infants - A Prospective Explorative Observational Study
Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- Point-of-care lung ultrasound - a rapid and reliable diagnostic tool for emergency physicians treating patients with acute dyspnea in high-volume emergency departments.Emergency radiology · 2025Observational
- Impact of positive end-expiratory pressure and recruitment maneuver on healthy lungs in dogs assessed by functional and anatomical monitoring methods.Frontiers in veterinary science · 2025Article
- Imaging the Lung in ARDS: A Primer.Respiratory care · 2024Review
- Electrical Impedance Tomography, Artificial Intelligence, and Variable Ventilation: Transforming Respiratory Monitoring and Treatment in Critical Care.Journal of personalized medicine · 2024Review
- Development of a nomogram based on the clinicopathological and CT features to predict the survival of primary pulmonary lymphoepithelial carcinoma patients.Respiratory research · 2024Article
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
- Lung Imaging and Artificial Intelligence in ARDS.Journal of clinical medicine · 2024Review
- A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.Respiratory research · 2023Article
- Prognostic Value of the Radiographic Assessment of Lung Edema Score in Mechanically Ventilated ICU Patients.Journal of clinical medicine · 2023Article
- A Survey on Deep Learning in COVID-19 Diagnosis.Journal of imaging · 2022Review
- Assessments of microvascular function in organ systems.American journal of physiology. Heart and circulatory physiology · 2022Review
- Lung aeration, ventilation, and perfusion imaging.Current opinion in critical care · 2022Review
- Computational lung modelling in respiratory medicine.Journal of the Royal Society, Interface · 2022Review
- Understanding the pathophysiology of typical acute respiratory distress syndrome and severe COVID-19.Expert review of respiratory medicine · 2022Review
- Effects of positive end-expiratory pressure on lung ultrasound patterns and their correlation with intracranial pressure in mechanically ventilated brain injured patients.Critical care (London, England) · 2022Observational
- Editorial: Lung Imaging in Respiratory Failure.Frontiers in physiology · 2022Article
- Aptamer-Based Diagnostic Systems for the Rapid Screening of TB at the Point-of-Care.Diagnostics (Basel, Switzerland) · 2021Review
- Extension of Collagen Deposition in COVID-19 Post Mortem Lung Samples and Computed Tomography Analysis Findings.International journal of molecular sciences · 2021Article
- Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning.Frontiers in physiology · 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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last years, imaging has played a key role in the diagnosis and monitoring and critical illness, including acute respiratory distress syndrome (ARDS). Chest X-ray (CXR) and computed tomography (CT) are the conventional techniques most performed in the critically ill patients, the latter being the gold standard to assess lung aeration in ARDS patients. In addition, two bedside techniques are now gaining popularity alongside the conventional ones: lung ultrasound (LUS) and electrical impedance tomography (EIT). These techniques do not involve the use of ionizing radiations, are non-invasive and relatively easy to use, and are under extensive investigation as a complement, and for some application a substitution of conventional techniques. At last, positron emission tomography (PET) and magnetic resonance imaging (MRI) can provide functional information on the lung and respiratory function, and are increasingly used in research to improve the understanding of the pathophysiological mechanisms underlying ARDS. The purpose of this review is to give an up-to-date overview of the conventional and emerging imaging techniques available the diagnosis and management of patients with ARDS.
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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.