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Artificial intelligence can detect left ventricular dilatation on contrast-enhanced thoracic computer tomography
Ashar Asif1, Pia F P Charters2, Charlotte A S Thompson2
1Medical School, University of Bristol, Bristol, UK.
Insights
An AI algorithm can detect left ventricular (LV) dilatation using non-ECG gated CT scans. This automated analysis provides sex-specific thresholds for screening dilated cardiomyopathy, aiding early detection.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Radiology
Background:
- Left ventricular (LV) dilatation is a key indicator of cardiac disease.
- Non-electrocardiogram (ECG) gated computed tomography (CT) is widely available but not typically used for precise cardiac chamber assessment.
- Cardiac magnetic resonance (CMR) is the reference standard for evaluating cardiac structure and function.
Purpose of the Study:
- To evaluate the diagnostic accuracy of an automated artificial intelligence (AI) algorithm for detecting left ventricular (LV) dilatation on non-ECG gated CT.
- To establish reference thresholds for LV dilatation using CT, validated against CMR.
- To explore the feasibility of using routine thoracic CT for screening cardiac conditions.
Main Methods:
- Retrospective analysis of 84 patients with contrast-enhanced thoracic CT and CMR within 31 days.
- AI algorithm performed automated segmentation of cardiac chambers on CT, measuring maximal LV diameter and volume.
- Receiver operator curve (ROC) analysis determined optimal CT diameter thresholds for LV dilatation, defined by CMR, with sex-specific values calculated.
Main Results:
- Automated LV diameter analysis was feasible in 92% of cases.
- Sex-specific thresholds for LV dilatation on CT were established: ≥55.5 mm for males and ≥49.7 mm for females, achieving ≥90% specificity.
- AI CT volumetry did not significantly enhance diagnostic performance compared to diameter measurements.
Conclusions:
- Fully automated AI analysis of LV diameter on non-ECG gated CT is feasible for detecting LV dilatation.
- Derived sex-specific CT diameter thresholds can facilitate routine screening for dilated cardiomyopathy.
- This approach offers a potential method for early cardiac screening during non-cardiac CT examinations.
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 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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