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.

中文翻译：

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超声心动图图像的深度学习分析以预测肥厚型心肌病患者的阳性基因型。

基因检测为肥厚型心肌病 (HCM) 患者的家族筛查策略、诊断和预后提供了宝贵的见解。另一方面，基因检测会带来社会经济和心理负担。因此，确定更可能具有阳性基因型的 HCM 患者非常重要。然而，基于临床和超声心动图参数的传统预测模型只能提供适度的准确性，并且受观察者内部和观察者间的变异性影响。因此，我们假设对超声心动图图像进行深度卷积神经网络（DCNN，一种深度学习）分析可以提高 HCM 患者阳性基因型的预测准确性。在每种情况下，我们都获得了胸骨旁短轴和长轴以及心尖 2、3、4 和 5 腔视图。我们采用 DCNN 算法根据输入的超声心动图图像预测阳性基因型。我们进行了 5 折交叉验证。我们使用了 2 个参考模型——Mayo HCM 基因型预测评分（Mayo 评分）和多伦多 HCM 基因型评分（多伦多评分）。我们比较了使用参考模型加上 DCNN 导出概率的组合模型与参考模型之间的接收器操作特征曲线 (AUC) 下面积。我们通过执行 1,000 次引导来计算 p 值。我们计算了敏感性、特异性、阳性预测值 (PPV) 和阴性预测值 (NPV)。此外，我们检查了净重分类改进。我们纳入了 99 名接受基因检测的 HCM 成人。总体而言，45 名患者 (45%) 具有阳性基因型。结合 Mayo 评分和 DCNN 衍生概率的新模型显着优于 Mayo 评分（AUC 0.86 [95% CI 0.79-0.93] vs. 0.72 [0.61-0.82]；p < 0.001）。类似地，与单独的多伦多分数相比，结合多伦多分数和 DCNN 衍生概率的新模型表现出更高的 AUC（AUC 0.84 [0.76-0.92] vs. 0.75 [0.65-0.85]；p = 0.03）。还实现了灵敏度、特异性、PPV 和 NPV 的改进，以及显着的净重分类改进。总之，与传统模型相比，我们结合传统模型和 DCNN 衍生模型的新模型在预测 HCM 患者的阳性基因型方面具有更高的准确性。与单独的多伦多分数相比，结合多伦多分数和 DCNN 衍生概率的新模型表现出更高的 AUC（AUC 0.84 [0.76-0.92] vs. 0.75 [0.65-0.85]；p = 0.03）。还实现了灵敏度、特异性、PPV 和 NPV 的改进，以及显着的净重分类改进。总之，与传统模型相比，我们结合传统模型和 DCNN 衍生模型的新模型在预测 HCM 患者的阳性基因型方面具有更高的准确性。与单独的多伦多分数相比，结合多伦多分数和 DCNN 衍生概率的新模型表现出更高的 AUC（AUC 0.84 [0.76-0.92] vs. 0.75 [0.65-0.85]；p = 0.03）。还实现了灵敏度、特异性、PPV 和 NPV 的改进，以及显着的净重分类改进。总之，与传统模型相比，我们结合传统模型和 DCNN 衍生模型的新模型在预测 HCM 患者的阳性基因型方面具有更高的准确性。