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Initial evaluation of a convolutional neural network used for noninvasive assessment of coronary artery disease severity from coronary computed tomography angiography data
Author(s) -
Podgorsak Alexander R.,
Sommer Kelsey N.,
Reddy Abhinay,
Iyer Vijay,
Wilson Michael F.,
Rybicki Frank J.,
Mitsouras Dimitrios,
Sharma Umesh,
Fujimoto Shinchiro,
Kumamaru Kanako K.,
Angel Erin,
Ionita Ciprian N.
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14339
Subject(s) - medicine , coronary artery disease , receiver operating characteristic , cad , radiology , convolutional neural network , cohort , computed tomography angiography , angiography , cardiac catheterization , artificial intelligence , computer science , engineering drawing , engineering
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.