Purpose

In this study, we try to consider the most common type of total anomalous pulmonary venous connection and established a machine learning-based prediction model for postoperative pulmonary venous obstruction by using clinical data and CT images jointly.

Method

Patients diagnosed with supracardiac TPAVC from January 1, 2009, to December 31, 2018, in Guangdong Province People’s Hospital were enrolled. Logistic regression were applied for clinical data features selection, while a convolutional neural network was used to extract CT images features. The prediction model was established by integrating the above two kinds of features for PVO prediction. And the proposed methods were evaluated using fourfold cross-validation.

Result

Finally, 131 patients were enrolled in our study. Results show that compared with traditional approaches, the machine learning-based joint method using clinical data and CT image achieved the highest average AUC score of 0.943. In addition, the joint method also achieved a higher sensitivity of 0.828 and a higher positive prediction value of 0.864.

Conclusion

Using clinical data and CT images jointly can improve the performance significantly compared with other methods that using only clinical data or CT images. The proposed machine learning-based joint method demonstrates the practicability of fully using multi-modality clinical data.

中文翻译：

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使用临床数据和CT图像的基于机器学习的肺静脉阻塞预测模型

目的

在这项研究中，我们尝试考虑最常见的总异常肺静脉连接类型，并结合临床数据和CT图像，建立了一种基于机器学习的术后肺静脉阻塞的预测模型。

方法

招募了2009年1月1日至2018年12月31日在广东省人民医院诊断为心动过速TPAVC的患者。Logistic回归用于临床数据特征选择，而卷积神经网络用于提取CT图像特征。综合上述两种特征进行PVO预测，建立了预测模型。并使用四重交叉验证对提出的方法进行了评估。

结果

最后，有131名患者参加了我们的研究。结果表明，与传统方法相比，使用临床数据和CT图像的基于机器学习的联合方法获得的最高平均AUC得分为0.943。此外，联合方法还实现了0.828的更高灵敏度和0.864的更高阳性预测值。

结论

与仅使用临床数据或CT图像的其他方法相比，共同使用临床数据和CT图像可以显着提高性能。所提出的基于机器学习的联合方法证明了充分利用多模态临床数据的实用性。