Artificial intelligence in pediatric echocardiography-Automated view classification and image anonymization in rare cardiac malformations on the example of borderline HLHS

Abstract

Abstract Background/Introduction Artificial intelligence (AI) has already much potential in adult echocardiography. However, there is still few experience in how to effectively implement AI methods in pediatric echocardiography. Since children with congenital heart defects show a large anatomical variance, AI-based image analysis tools adapted to this group are necessary. For the fundamentally needed steps of an automatic image data anonymization and a view classification there are still no reliable tools available. Purpose We have developed an anonymization tool for pediatric echocardiography data. For effective automated analysis of echocardiographies in children, we established an algorithm using bHLHS as an example that requires only small numbers of patients for a good view classification accuracy. Thus, we expect that the algorithm will also be applicable to other cardiac malformations. Method Raw data were obtained from visage imaging platforms and echocardiography examinations were conducted on Siemens machines. An anonymization tool was developed using the Python programming language and the Tkinter library. It converts medical images and crops the embedded data from the non-anatomical top region. The classification neural network was evaluated with data obtained from 5 patients with bHLHS. After data cleaning, each patient's image is labelled according to different Echo views: 4CH, SA, and LA, creating a total of 6600 images of 3 separate views. Due to the limited echocardiography dataset, a data augmentation approach was employed to improve the generalization of the AI model. The transfer learning approach was used to develop the AI model, which utilized a pre-trained VGG 13 model that was modified to classify the three different views of the pediatric echocardiography dataset. The modified model was fine-tuned on the dataset with a GPU-enhanced Intel i9 platform and Pycharm 2022.2.4. We use 70% dataset for training, 20% dataset for testing, and 10% dataset for validating. Results Table 1 shows that the expert clinician validated the anonymization tool by comparing identified patient information in the raw image and anonymized image. The trained pediatric echocardiography view classification model achieved an overall accuracy of 0.776 with precision values of 0.725, 0.889, and 0.775 for LA, SA and 4CH classes, respectively. The F1 score values were 0.835, 0.690, and 0.778 for the same classes. ROC curve were plotted (Figure 1) to visualize the performance of the model. Conclusion This study shows the importance of data anonymization and view classification in automating the diagnosis of HLHS using echocardiography images. The developed deep learning algorithm showed promising performance in accurately classifying different views of echocardiography images for diagnosing HLHS. Future studies can build upon this work by developing a complete diagnostic system to automatically analyze cardiac structures of patients with cardiac malformations.