Diagnostic accuracy of 3D deep-learning-based fully automated estimation of patient-level minimum fractional flow reserve from coronary computed tomography angiography. (23rd June 2019)

• Record Type: Journal Article
• Authors: Kumamaru, Kanako K; Fujimoto, Shinichiro; Otsuka, Yujiro; Kawasaki, Tomohiro; Kawaguchi, Yuko; Kato, Etsuro; Takamura, Kazuhisa; Aoshima, Chihiro; Kamo, Yuki; Kogure, Yosuke; Inage, Hidekazu; Daida, Hiroyuki; Aoki, Shigeki
• Is Part Of: European heart journal. Volume 21:Number 4(2020)
• Page Start: 437
• Page End: 445
• Publication Date: 2019-06-23
• Subjects: computed tomography -- coronary artery disease -- deep learning -- fractional flow reserve; Cardiovascular system -- Imaging -- Periodicals; Heart -- Imaging -- Periodicals; 616.10754
• DOI: 10.1093/ehjci/jez160
• Languages: English
• ISSNs: 2047-2404

Abstract

Abstract: Aims: Although deep-learning algorithms have been used to compute fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA), no study has achieved 'fully automated' (i.e. free from human input) FFR calculation using deep-learning algorithms. The purpose of the study was to evaluate the accuracy of a fully automated 3D deep-learning model for estimating minimum FFR from CCTA data, with invasive FFR as the reference standard. Methods and results: This retrospective study of 1052 patients included 131 patients whose CCTA studies showed 30–90% stenosis and underwent invasive FFR (abnormal FFR observed in 72/131, 55%), and 921 patients who underwent clinically indicated CCTA without invasive FFR. We designed a fully automated 3D deep-learning model that inputs CCTA data and outputs minimum FFR without requiring human input. The model comprised a series of deep-learning algorithms: a conditional generative adversarial network, a 3D convolutional ladder network, and two independent neural networks with integrated virtual adversarial training. We used Monte Carlo cross-validation to evaluate the accuracy of the model for estimating FFR, with invasive FFR as the reference standard. The deep-learning FFR achieved area under the receiver-operating characteristic curve of 0.78 for detection of abnormal FFR; and was significantly higher than for visually determined CCTA >50% stenosis (area under the curve = 0.56). The deep-learning FFR model achieved 76% accuracy for detecting abnormal FFR, with sensitivity of 85% (79–89%) and specificity of 63% (54–70%). Conclusion: The 3D deep-learning model, which performs fully automatic estimation of minimum FFR from cardiac CT data, achieved 76% accuracy in detecting abnormal FFR.