Evidence map›Paper›PMID 42699033›Full record

ArticleEuropean heart journal. Imaging methods and practice2026

AI-based pulmonary artery to ascending aorta ratio on non-contrast CT for pulmonary hypertension: diameter vs. volume assessment.

Turki Nasser Alnasser, Alireza Hokmabadi, Ahmed Maiter, Michael Sharkey, Christopher Johns, Smitha Rajaram, David G Kiely, Samer Alabed, Andrew J Swift

Abstract read
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Article in European heart journal. Imaging methods and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Turki Nasser AlnasserSchool of Medicine and Population Health, The University of Sheffield, Sheffield S10 2TN, UK.ORCID https://orcid.org/0009-0004-8014-4924
Alireza HokmabadiSchool of Medicine and Population Health, The University of Sheffield, Sheffield S10 2TN, UK.
Ahmed MaiterSchool of Medicine and Population Health, The University of Sheffield, Sheffield S10 2TN, UK.ORCID https://orcid.org/0000-0002-4999-2608
Michael SharkeySchool of Medicine and Population Health, The University of Sheffield, Sheffield S10 2TN, UK.
Christopher JohnsDepartment of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK.ORCID https://orcid.org/0000-0003-3724-0430
Smitha RajaramDepartment of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK.
David G KielySchool of Medicine and Population Health, The University of Sheffield, Sheffield S10 2TN, UK.
Samer AlabedSchool of Medicine and Population Health, The University of Sheffield, Sheffield S10 2TN, UK.ORCID https://orcid.org/0000-0002-9960-7587
Andrew J SwiftSchool of Medicine and Population Health, The University of Sheffield, Sheffield S10 2TN, UK.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Aims: To assess the diagnostic accuracy of a deep learning (DL) model for quantifying the pulmonary artery (PA) and ascending aorta (AAo) diameters and volumes on non-contrast computed tomography (CT) scans for detecting pulmonary hypertension (PH). Methods and results: The PA and AAo were segmented using a validated DL model on non-contrast CT scans. PA/AAo ratios were quantified using diameter and volume measurements. Testing was performed using two independent patient cohorts. A first cohort ( Conclusion: The PA/AAo volume ratio on non-contrast CT demonstrates high diagnostic accuracy for detecting PH and could assist with opportunistic detection of PH in patients undergoing routine chest imaging.

Indexed as

ascending aortaCTdiagnosticdiameterpulmonary arterypulmonary hypertensionvolume

Identifiers

PMID42699033
PMCPMC13543682

What OpenQuestion holds

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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.