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

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

Research output: Contribution to journalArticlepeer-review

1 Citation (SciVal)

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 (n = 84). LV dilatation was defined against age-, sex-and body surface area-specific values for CMR. CTs underwent automated artificial intelligence(AI)derived analysis that segmented ventricular chambers, presenting maximal LV diameter and volume. Area under the receiver operator curve (AUC-ROC) analysis identified CT thresholds with ≥90% sensitivity and highest specificity and ≥90% specificity with highest sensitivity. Youden’s Index was used to identify thresholds with optimised sensitivity and specificity. 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 contrastenhanced 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 costeffective manner.

Original languageEnglish
Article number20210852
JournalBritish Journal of Radiology
Volume95
Issue number1138
Early online date18 Mar 2022
DOIs
Publication statusPublished - 1 Sep 2022
Externally publishedYes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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