ArticleRadiology. Artificial intelligence2022
Automated Deep Learning Analysis for Quality Improvement of CT Pulmonary Angiography.
Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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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.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- A randomised controlled trial comparing three clinical administration strategies in spectral detector CT pulmonary angiography with low contrast medium dose.European radiology · 2025Trial
- Clinical value of the 70-kVp ultra-low-dose CT pulmonary angiography with deep learning image reconstruction.European radiology · 2026Article
- The role of artificial intelligence and mobile health in diagnosis and management of pulmonary arterial hypertension.International journal of cardiology. Congenital heart disease · 2025Review
- High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis.NPJ digital medicine · 2025Article
- The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection.Frontiers in medicine · 2025Review
- Mapping the Spatial Extent of Hypoperfusion in Chronic Thromboembolic Pulmonary Hypertension Using Multienergy CT.Radiology. Cardiothoracic imaging · 2023Article
- Imaging AI in Practice: Introducing the Special Issue.Radiology. Artificial intelligence · 2022Article
- Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
CT pulmonary angiography (CTPA) is the first-line imaging test for evaluation of acute pulmonary emboli. However, diagnostic quality is heterogeneous across institutions and is frequently limited by suboptimal pulmonary artery (PA) contrast enhancement. In this retrospective study, a deep learning algorithm for measuring enhancement of the central PAs was developed and assessed for feasibility of its use in quality improvement of CTPA. In a convenience sample of 450 patients, automated measurement of CTPA enhancement showed high agreement with manual radiologist measurement (
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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.