Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy

Frontiers in Cardiovascular Medicine

Michael A. Fifer¹  Kenya Kusunose²  Masataka Sata²  Yoshihiko Raita³  Kohei Hasegawa³  Akihiro Haga⁴  Mathew S. Maurer⁵  Sae X. Morita⁵  Yuichi J. Shimada⁵  Muredach P. Reilly⁶ 

[1] Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States;Department of Cardiovascular Medicine, Tokushima University, Tokushima, Japan;Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States;Department of Medical Image Informatics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan;Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States;Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States;Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, NY, United States;

关键词: hypertrophic cardiomyopathy;    echocardiography;    deep learning;    genotype;    prediction;   

DOI  :  10.3389/fcvm.2021.669860

来源: Frontiers

【 摘 要 】

Genetic testing provides valuable insights into family screening strategies, diagnosis, and prognosis in patients with hypertrophic cardiomyopathy (HCM). On the other hand, genetic testing carries socio-economical and psychological burdens. It is therefore important to identify patients with HCM who are more likely to have positive genotype. However, conventional prediction models based on clinical and echocardiographic parameters offer only modest accuracy and are subject to intra- and inter-observer variability. We therefore hypothesized that deep convolutional neural network (DCNN, a type of deep learning) analysis of echocardiographic images improves the predictive accuracy of positive genotype in patients with HCM. In each case, we obtained parasternal short- and long-axis as well as apical 2-, 3-, 4-, and 5-chamber views. We employed DCNN algorithm to predict positive genotype based on the input echocardiographic images. We performed 5-fold cross-validations. We used 2 reference models—the Mayo HCM Genotype Predictor score (Mayo score) and the Toronto HCM Genotype score (Toronto score). We compared the area under the receiver-operating-characteristic curve (AUC) between a combined model using the reference model plus DCNN-derived probability and the reference model. We calculated the p-value by performing 1,000 bootstrapping. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, we examined the net reclassification improvement. We included 99 adults with HCM who underwent genetic testing. Overall, 45 patients (45%) had positive genotype. The new model combining Mayo score and DCNN-derived probability significantly outperformed Mayo score (AUC 0.86 [95% CI 0.79–0.93] vs. 0.72 [0.61–0.82]; p < 0.001). Similarly, the new model combining Toronto score and DCNN-derived probability exhibited a higher AUC compared to Toronto score alone (AUC 0.84 [0.76–0.92] vs. 0.75 [0.65–0.85]; p = 0.03). An improvement in the sensitivity, specificity, PPV, and NPV was also achieved, along with significant net reclassification improvement. In conclusion, compared to the conventional models, our new model combining the conventional and DCNN-derived models demonstrated superior accuracy to predict positive genotype in patients with HCM.

【 授权许可】

CC BY