ArticleThe British journal of radiology2023
Assessing the role of an artificial intelligence assessment tool for thoracic aorta diameter on routine chest CT.
Article in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Automated Aortic Quantification Based on Artificial Intelligence: Validation Using Contrast-Enhanced and Non-Contrast CT Scans from the Same Session.Bioengineering (Basel, Switzerland) · 2026Article
- The role of AI assistance in the evaluation of unenhanced chest CT scans in an emergency setting.Polish journal of radiology · 2026Article
- Repeatability of AI-based, automatic measurement of vertebral and cardiovascular imaging biomarkers in low-dose chest CT: the ImaLife cohort.European radiology · 2025Article
- Aorta unveiled: the crucial role of imaging in diagnosing and managing aortic disease-a review.European heart journal. Imaging methods and practice · 2025Review
- Deep Learning-Based Aortic Diameter Measurement in Traumatic Hemorrhage Using Shallow Attention Network: A Path Forward.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence-powered solutions for automated aortic diameter measurement in computed tomography: a narrative review.Annals of translational medicine · 2024Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo assess the diagnostic accuracy and clinical impact of automated artificial intelligence (AI) measurement of thoracic aorta diameter on routine chest CT.
methodsA single-centre retrospective study involving three cohorts. 210 consecutive ECG-gated CT aorta scans (mean age 75 ± 13) underwent automated analysis (AI-Rad Companion Chest CT, Siemens) and were compared to a reference standard of specialist cardiothoracic radiologists for accuracy measuring aortic diameter. A repeated measures analysis tested reporting consistency in a second cohort (29 patients, mean age 61 ± 17) of immediate sequential pre-contrast and contrast CT aorta acquisitions. Potential clinical impact was assessed in a third cohort of 197 routine CT chests (mean age 66 ± 15) to document potential clinical impact.
resultsAI analysis produced a full report in 387/436 (89%) and a partial report in 421/436 (97%). Manual
conclusionAI has good agreement with expert readers at the mid-ascending aorta and has high specificity, but low sensitivity, at detecting dilated aortas on non-dedicated chest CTs. ADVANCES IN KNOWLEDGE: An AI tool may improve the detection of previously unknown thoracic aorta dilatation on chest CTs
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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.