Key result
An automated artificial intelligence algorithm applied to non-ECG-gated contrast-enhanced thoracic CT detected left ventricular dilatation with an area under the curve of 0.714 relative to cardiac magnetic resonance imaging.
Why the study?
To assess the diagnostic accuracy of an automated algorithm to detect left ventricular dilatation on non-ECG gated CT, using CMR as the reference standard.
Does an automated AI algorithm accurately detect left ventricular dilatation on non-ECG gated contrast-enhanced thoracic CT compared to cardiac magnetic resonance imaging?
Observational (n=84)
Double-blind
No
Does an automated AI algorithm accurately detect left ventricular dilatation on non-ECG gated contrast-enhanced thoracic CT compared to cardiac magnetic resonance imaging?
A fully automated AI algorithm can feasibly detect left ventricular dilatation on routine non-ECG gated contrast-enhanced thoracic CT with high specificity, offering a potential screening tool for dilated cardiomyopathy.
Answers are grounded in this paper and cite their sources.
May support AI detection of LV dilatation on routine CT; hypothesis-generating and requires larger validation before clinical adoption.
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: = 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 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.
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Asif et al. (2022) conducted an observational in Left ventricular dilatation (n=84). Automated AI-derived analysis of non-ECG gated contrast-enhanced thoracic CT vs. Cardiac magnetic resonance (CMR) imaging was evaluated on Detection of left ventricular dilatation (AUC-ROC) (AUC 0.714, 95% CI 0.595-0.833, p=0.001). An automated artificial intelligence algorithm applied to non-ECG-gated contrast-enhanced thoracic CT detected left ventricular dilatation with an area under the curve of 0.714 relative to cardiac magnetic resonance imaging.
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