Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning

• URL: http://arxiv.org/abs/2306.03835v1
• Date: Tue, 6 Jun 2023 16:25:29 GMT
• Title: Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning
• Authors: Yiman Liu and Qiming Huang and Xiaoxiang Han and Tongtong Liang and Zhifang Zhang and Lijun Chen and Jinfeng Wang and Angelos Stefanidis and Jionglong Su and Jiangang Chen and Qingli Li and Yuqi Zhang
• Abstract summary: This paper aims to study a deep learning method based on cardiac ultrasound video to assist in atrial septal defect diagnosis. We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score.
• Score: 14.62565592495898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. Materials and methods: We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). We propose an echocardiography video-based atrial septal defect diagnosis system. In our model, we present a block random selection, maximal agreement decision and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. Results: We validate our model using our private dataset by five-cross validation. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score. Conclusion: The proposed model is multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.

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