ReviewRespirology (Carlton, Vic.)2022
Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry.
Review in Respirology (Carlton, Vic.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- Computed tomography-based radiomics improves non-invasive diagnosis of Pneumocystis jirovecii pneumonia in non-HIV patients: a retrospective study.BMC pulmonary medicine · 2024Trial
- Review
- Applications of AI/ML in accelerating the development of pulmonary drug delivery system.Acta pharmaceutica Sinica. B · 2026Review
- Evaluating Deployment-related Respiratory Diseases in Military Veterans.Annals of the American Thoracic Society · 2025Review
- 3DMPR - a robust morphological approach for applying phase retrieval in proximity to highly attenuating objects in computed tomography.Journal of synchrotron radiation · 2025Article
- Contrast-Enhanced Cardiac Computed Tomography and the Presence of Intravascular Air: A Patient Safety Study.Journal of clinical medicine · 2025Article
- Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.European radiology · 2025Article
- Future Applications of Cardiothoracic CT.Radiology · 2025Review
- Functional Lung Imaging Identifies Peripheral Ventilation Changes in ꞵ-ENaC Mice.Respirology (Carlton, Vic.) · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 2025Review
- A low-dose CT reconstruction method using sub-pixel anisotropic diffusion.Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Application research of artificial intelligence software in the analysis of thyroid nodule ultrasound image characteristics.PloS one · 2025Article
- Chest computed tomography for patients with sepsis in the emergency intensive care unit.Scientific data · 2024Article
- Automatic Defects Recognition of Lap Joint of Unequal Thickness Based on X-Ray Image Processing.Materials (Basel, Switzerland) · 2024Article
- Blood microRNA testing in participants with suspicious low-dose CT findings: follow-up of the BioMILD lung cancer screening trial.The Lancet regional health. Europe · 2024Article
- Current understanding of the impact of United States military airborne hazards and burn pit exposures on respiratory health.Particle and fibre toxicology · 2024Review
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Assessment of respiratory mechanics and X-ray velocimetry functional imaging in two cystic fibrosis rat models.Scientific reports · 2024Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation.
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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.