Atrial Septal Defect Detection in Children Based on Ultrasound Video
  Using Multiple Instances Learning
Yiman Liu Qiming Huang Xiaoxiang Han Tongtong Liang Zhifang Zhang Lijun Chen Jinfeng Wang Angelos Stefanidis Jionglong Su Jiangang Chen Qingli Li Yuqi Zhang
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.