Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Morita S

PROVIDER: S-EPMC8429777 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an echocardiogram. Currently, there is no consensus whether the mislabeled samples can be used to create a feasible clinical prediction model of ejection fraction (EF). The aim of this study was to test two types of input methods for the classification of images, and to test the accuracy of the prediction model for EF in a learning database containing mislabeled images that were not checked by observers. We enrolled 340 patients with five standard views (long axis, short axis, 3-chamber view, 4-chamber view and 2-chamber view) and 10 images in a cycle, used for training a convolutional neural network to classify views (total 17,000 labeled images). All DICOM images were rigidly registered and rescaled into a reference image to fit the size of echocardiographic images. We employed 5-fold cross validation to examine model performance. We tested models trained by two types of data, averaged images and 10 selected images. Our best model (from 10 selected images) classified video views with 98.1% overall test accuracy in the independent cohort. In our view classification model, 1.9% of the images were mislabeled. To determine if this 98.1% accuracy was acceptable for creating the clinical prediction model using echocardiographic data, we tested the prediction model for EF using learning data with a 1.9% error rate. The accuracy of the prediction model for EF was warranted, even with training data containing 1.9% mislabeled images. The CNN algorithm can classify images into five standard views in a clinical setting. Our results suggest that this approach may provide a clinically feasible accuracy level of view classification for the analysis of echocardiographic data.

Project description:There has been limited data addressing outcomes of extensive septal myectomy in Chinese patients with hypertrophic obstructive cardiomyopathy (HOCM). In this study, the objective was to evaluate the clinical and echocardiographic outcomes of extensive septal myectomy in a relative large number of Chinese HOCM patients over long-term follow-up.We retrospectively studied 139 consecutive HOCM patients (age 43 ± 15 years, 37 % male) who underwent extensive left ventricular septal myectomy. During the perioperative period, all patients were examined by echocardiography. All-cause death and cardiac death were considered as primary endpoints during follow-up. Perioperative data was obtained by retrospective review of institutional surgical databases. Follow-up data of echocardiography and clinical status was recorded through outpatient interview.Perioperative events consisted of arrhythmia, retraction injury to aortic valve leaflets, pleural effusion, and hemodialysis and the use of intra-aortic balloon pump. There was no in-hospital mortality. The follow-up period averaged 5.6 ± 0.9 years and overall survivals were 100.0, 99.3, 99.3, 98.5 and 97.8 % at 1, 2, 3, 4 and 5 years, respectively. Left ventricular outflow tract (LVOT) gradient decreased form preoperative 84 ± 17 mmHg to 12 ± 3 mmHg at 2.5 years after surgery and it further reduced to 6 ± 3 mmHg at 5 years after surgery (P < 0.05). Compared with the preoperative levels, interventricualr septal thickness decreased by 32 % while diastole left ventricular inner diameter approximately increased by 10 % and ejection fraction (EF) was significantly elevated during follow-up (P < 0.05). By echocardiography detection, mitral regurgitation was ameliorated for HOCM patients after surgery. There was significant improvement in New York Heart Association (NYHA) class. The proportion of NYHA III and IV decreased from preoperative 58 to 19 % at 2.5 years after surgery and it reduced to 11 % at 5 years after operation.Extensive septal myectomy offers minimal operative risk and provides long-term relief for LVOT obstruction in Chinese HOCM patients.

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

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