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Published on: September 22, 2023
Initial evaluation of a convolutional neural network used for noninvasive assessment of coronary artery disease
Alexander R Podgorsak1, Kelsey N Sommer1, Abhinay Reddy1
1From the Canon Stroke and Vascular Research Center, 875 Ellicott Street, Buffalo, NY, 14222, USA.
Insights
A trained machine learning model using coronary computed tomography angiography (CTA) data improved coronary artery disease (CAD) diagnosis accuracy. The AI model showed noninferior performance compared to human interpretation of CT-derived fractional flow reserve (FFR).
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Medical Diagnostics
Background:
- Coronary computed tomography angiography (CTA) offers high sensitivity for coronary artery disease (CAD) detection but moderate accuracy.
- This can lead to increased rates of invasive cardiac catheterization.
- Improving the diagnostic accuracy of non-invasive methods is crucial for patient management.
Purpose of the Study:
- To evaluate the efficacy of a trained machine learning algorithm utilizing coronary CTA data for enhancing CAD diagnosis accuracy.
- To compare the diagnostic performance of the AI model against traditional methods and human interpretation.
Main Methods:
- A convolutional neural network (CNN) was trained on 64-patient coronary CTA datasets.
- The model predicted CAD severity, with invasive fractional flow reserve (FFR) as the ground truth.
- Performance was assessed using classification accuracy and AUROC, comparing the CNN to percent diameter stenosis (%DS) and human-operated CT-derived FFR.
Main Results:
- The trained CNN achieved 80.9% classification accuracy, outperforming user-operated CT-derived FFR software (72.4%).
- The CNN demonstrated a superior AUROC of 0.862 compared to %DS (0.807) and human-operated CT-derived FFR (0.758).
Conclusions:
- A trained neural network achieved noninferior classification accuracy and AUROC compared to human operators using CT-derived FFR software.
- The AI model's AUROC also compared favorably against clinical decision-making based on %DS, suggesting its potential in CAD diagnosis.
Purpose:
Coronary computed tomography angiography (CTA) has one of the highest diagnostic sensitivities for detection of the significance of coronary artery disease (CAD); however, sensitivity is moderate and may result in increased catheterization rates. We performed an efficacy study to determine whether a trained machine learning algorithm that uses coronary CTA data may improve CAD diagnosis accuracy.
Methods:
Sixty-four-patient image datasets based on coronary CTA were retrospectively collected to generate eight views considering 45° increments around the coronary artery centerline. The dataset was randomly split into training and testing cohorts. Invasive FFR measurements were used as ground truth labels. A convolutional neural network (CNN) was trained and the model's capacity to predict severity of CAD was assessed on the testing cohort. Classification accuracy and area under the receiver operating characteristic curve (AUROC) analysis were performed. Similar CAD severity classification accuracy and AUROC analyses were performed using only percent diameter stenosis (%DS) and CT-derived FFR performed by 13 operators with various levels of expertise.
Results:
Classification accuracy over the test cohort was 80.9% using the trained network and 72.4% using the user-operated CT-derived FFR software. AUROC over the test cohort was 0.862 using the trained network, 0.807 using %DS, and 0.758 using the human-operated CT-derived FFR software.
Conclusions:
A trained neural network compared noninferiorly in-terms of classification accuracy and AUROC with human operators of a CT-derived FFR software, and in-terms of AUROC with clinical decision-making using %DS.
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