Kavli Affiliate: Zeeshan Ahmed
| First 5 Authors: Robert Avram, Jeffrey E. Olgin, Alvin Wan, Zeeshan Ahmed, Louis Verreault-Julien
| Summary:
Coronary heart disease (CHD) is the leading cause of adult death in the
United States and worldwide, and for which the coronary angiography procedure
is the primary gateway for diagnosis and clinical management decisions. The
standard-of-care for interpretation of coronary angiograms depends upon ad-hoc
visual assessment by the physician operator. However, ad-hoc visual
interpretation of angiograms is poorly reproducible, highly variable and bias
prone. Here we show for the first time that fully-automated angiogram
interpretation to estimate coronary artery stenosis is possible using a
sequence of deep neural network algorithms. The algorithmic pipeline we
developed–called CathAI–achieves state-of-the art performance across the
sequence of tasks required to accomplish automated interpretation of
unselected, real-world angiograms. CathAI (Algorithms 1-2) demonstrated
positive predictive value, sensitivity and F1 score of >=90% to identify the
projection angle overall and >=93% for left or right coronary artery angiogram
detection, the primary anatomic structures of interest. To predict obstructive
coronary artery stenosis (>=70% stenosis), CathAI (Algorithm 4) exhibited an
area under the receiver operating characteristic curve (AUC) of 0.862 (95% CI:
0.843-0.880). When externally validated in a healthcare system in another
country, CathAI AUC was 0.869 (95% CI: 0.830-0.907) to predict obstructive
coronary artery stenosis. Our results demonstrate that multiple purpose-built
neural networks can function in sequence to accomplish the complex series of
tasks required for automated analysis of real-world angiograms. Deployment of
CathAI may serve to increase standardization and reproducibility in coronary
stenosis assessment, while providing a robust foundation to accomplish future
tasks for algorithmic angiographic interpretation.
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