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  1. AU="Thompson, Charlotte A S"
  2. AU="Dubbel, Polly"
  3. AU="Ten Bosch, Nora"
  4. AU="Giménez-Arnau, Ana Maria"
  5. AU=Maul Robert W.
  6. AU="Ivn Prez-MaldonadoauthorLaboratorio de Toxicologa Molecular, Centro de Investigacin Aplicada en Ambiente y Salud (CIAAS), Coordinacin para la Innovacin y Aplicacin de la Ciencia y la Tecnologa (CIACYT), Universidad Autnoma de San Luis Potos, MexicoFacultad de Medicina, Universidad Autnoma de San Luis Potos, San Luis Potos, MexicoFacultad de Enfermera, Universidad Autnoma de Zacatecas, Mexico"
  7. AU="Hansen, Kristian Schultz"
  8. AU="Davenport, Bennett"

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Artikel ; Online: Artificial intelligence can detect left ventricular dilatation on contrast-enhanced thoracic computer tomography relative to cardiac magnetic resonance imaging.

Asif, Ashar / Charters, Pia F P / Thompson, Charlotte A S / Komber, Hend M E I / Hudson, Benjamin J / Rodrigues, Jonathan Carl Luis

The British journal of radiology

2022  Band 95, Heft 1138, Seite(n) 20210852

Abstract: Objectives: To assess the diagnostic accuracy of an automated algorithm to detect left ventricular (LV) dilatation on non-ECG gated CT, using cardiac magnetic resonance (CMR) as reference standard.: Methods: Consecutive patients with contrast- ... ...

Abstract Objectives: To assess the diagnostic accuracy of an automated algorithm to detect left ventricular (LV) dilatation on non-ECG gated CT, using cardiac magnetic resonance (CMR) as reference standard.
Methods: Consecutive patients with contrast-enhanced CT thorax and CMR within 31 days (2016-2020) were analysed (
Results: Automated diameter analysis was feasible in 92% of cases (77/84; 45 men, age 61 ± 14 years, mean CT to CMR interval 10 ± 8 days). Relative to CMR as a reference standard, 45% had LV dilatation. In males, an automated LV diameter measurement of ≥55.5 mm was ≥90% specific for CMR-defined LV dilatation (positive predictive value (PPV) 85.7%, negative predictive value (NPV) 61.2%, accuracy 68.9%). In females, an LV diameter of ≥49.7 mm was ≥90% specific for CMR-defined LV dilatation (PPV 66.7%, NPV 73.1%, accuracy 71.9%). AI CT volumetry data did not significantly improve AUC performance.
Conclusion: Fully automated AI-derived analysis LV dilatation on routine unselected non-gated contrast-enhanced CT thorax studies is feasible. We have defined thresholds for the detection of LV dilatation on CT relative to CMR, which could be used to routinely screen for dilated cardiomyopathy at the time of CT.
Advances in knowledge: We show, for the first time, that a fully-automated AI-derived analysis of maximal LV chamber axial diameter on non-ECG-gated thoracic CT is feasible in unselected real-world cases and that the derived measures can predict LV dilatation relative to cardiac magnetic resonance imaging, the non-invasive reference standard for determining cardiac chamber size. We have derived sex-specific cut-off values to screen for LV dilatation on routine contrast-enhanced thoracic CT. Future work should validate these thresholds and determine if technology can alter clinical outcomes in a cost-effective manner.
Mesh-Begriff(e) Aged ; Artificial Intelligence ; Computers ; Dilatation ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Reproducibility of Results ; Stroke Volume ; Tomography, X-Ray Computed/methods
Sprache Englisch
Erscheinungsdatum 2022-03-18
Erscheinungsland England
Dokumenttyp Journal Article
ZDB-ID 2982-8
ISSN 1748-880X ; 0007-1285
ISSN (online) 1748-880X
ISSN 0007-1285
DOI 10.1259/bjr.20210852
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