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Updated: Jul 3, 2026
High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
Published on: December 16, 2022
An artificial intelligence model to detect abnormal ejection fraction from non-contrast chest computed tomography:
Jayant Raikhelkar1, Zilong Bai2, Ashley N Beecy3
1Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center/NewYork-Presbyterian Hospital, New York, NY, USA.
Insights
An AI model can now predict abnormal left ventricular ejection fraction (LVEF) from chest CT scans. This opportunistic screening tool aids early heart failure detection in asymptomatic patients.
Area of Science:
- Artificial Intelligence in Medical Imaging
- Cardiology and Cardiovascular Diseases
- Radiology and Diagnostic Imaging
Background:
- Heart failure (HF) is a significant global health burden, with many patients having early systolic dysfunction remaining asymptomatic.
- Current medical therapies can prevent HF progression if initiated early, but diagnosis is often delayed in asymptomatic individuals.
Purpose of the Study:
- To develop and validate an artificial intelligence (AI) model for predicting abnormal left ventricular ejection fraction (LVEF) using non-contrast chest CT scans.
- To explore the potential of AI for opportunistic screening of systolic heart failure from routine imaging.
Main Methods:
- A multi-institutional dataset of 34,058 non-contrast chest CT scans and echocardiogram reports was used for training.
- The AI classification model predicted abnormal LVEF (EF < 50%) versus normal.
- External validation was performed on 8,110 additional paired CT and echocardiogram results.
Main Results:
- The AI model achieved an area under the receiver operating characteristic curve (AUROC) of 0.786 on the test set and 0.762 on external validation for detecting abnormal LVEF.
- The AI model demonstrated superior accuracy and efficiency compared to expert radiologists.
- Interpretable visualizations highlighted imaging features associated with reduced LVEF.
Conclusions:
- An AI model can accurately predict abnormal LVEF from static, non-gated, non-contrast chest CT scans.
- This novel application offers a promising method for opportunistic screening and early detection of systolic heart failure.
- The AI technology may help reduce the diagnostic gap and improve outcomes for asymptomatic HF patients.
Aims:
Heart failure (HF), a major global health challenge, affects millions worldwide and poses substantial healthcare and economic burdens. It is estimated that a large proportion of those with early systolic dysfunction remain asymptomatic at a stage when guideline-directed medical therapies have been shown to prevent disease progression. To develop an artificial intelligence (AI) model capable of predicting abnormal left ventricular ejection fraction (EF) directly from static, non-gated, non-contrast chest computed tomography (CT) scans as a form of opportunistic screening.
Methods And Results:
Using a multi-institutional dataset of 34 058 paired non- contrast CT images and echocardiogram reports from two academic centres, we trained our model of classification for predicting left-ventricle ejection fraction (LVEF) categories: abnormal EF (EF < 50%) vs. normal on 25 948 studies. We validated the model on 8110 paired chest CT and echocardiogram results from a separate institution. The model achieved an area under the receiver operating characteristic (AUROC) curve of 0.786 on the hold-out test set and 0.762 on external validation to detect an abnormal EF (<50%). Beyond strong predictive performance, the AI model surpassed expert radiologists in both accuracy and efficiency and provided interpretable visualizations highlighting imaging features linked to reduced LVEF.
Conclusion:
In this study, we developed and validated an AI model capable of predicting abnormal LVEF directly from static, non-gated, non-contrast chest CT scans, a novel application for an imaging modality typically used for unrelated indications as a form of opportunistic screening. This technology holds significant promise for early detection of systolic HF, reducing the diagnostic gap, and improving outcomes in asymptomatic HF patients.
