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AuthorRahman, Tawsifur
AuthorAl-Ruweidi, Mahmoud Khatib A.A.
AuthorSumon, Md Shaheenur Islam
AuthorKamal, Reema Yousef
AuthorChowdhury, Muhammad E.H.
AuthorYalcin, Huseyin C.
Available date2023-11-05T05:39:06Z
Publication Date2023-01-01
Publication NameIEEE Access
Identifierhttp://dx.doi.org/10.1109/ACCESS.2023.3316719
CitationT. Rahman, M. K. A. A. Al-Ruweidi, M. S. I. Sumon, R. Y. Kamal, M. E. H. Chowdhury and H. C. Yalcin, "Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study," in IEEE Access, vol. 11, pp. 110375-110390, 2023, doi: 10.1109/ACCESS.2023.3316719.
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85173057780&origin=inward
URIhttp://hdl.handle.net/10576/48993
AbstractCongenital heart defects (CHDs) are a leading cause of death in infants under 1 year of age. Prenatal intervention can reduce the risk of postnatal serious CHD patients, but current diagnosis is based on qualitative criteria, which can lead to variability in diagnosis between clinicians. Objectives: To detect morphological and temporal changes in cardiac ultrasound (US) videos of fetuses with hypoplastic left heart syndrome (HLHS) using deep learning models. A small cohort of 9 healthy and 13 HLHS patients were enrolled, and ultrasound videos at three gestational time points were collected. The videos were preprocessed and segmented to cardiac cycle videos, and five different deep learning CNN-LSTM models were trained (MobileNetv2, ResNet18, ResNet50, DenseNet121, and GoogleNet). The top-performing three models were used to develop a novel stacking CNN-LSTM model, which was trained using five-fold cross-validation to classify HLHS and healthy patients. The stacking CNN-LSTM model outperformed other pre-trained CNN-LSTM models with the accuracy, precision, sensitivity, F1 score, and specificity of 90.5%, 92.5%, 92.5%, 92.5%, and 85%, respectively for video-wise classification, and with the accuracy, precision, sensitivity, F1 score, and specificity of 90.5%, 92.5%, 92.5%, 92.5%, and 85%, respectively for subject-wise classification using ultrasound videos. This study demonstrates the potential of using deep learning models to classify CHD prenatal patients using ultrasound videos, which can aid in the objective assessment of the disease in a clinical setting.
SponsorThis study was funded by Qatar National Research Fund (QNRF), National Priorities Research Program (NPRP 10-0123-170222). The open access publication of this article was funded by the Qatar National Library.
Languageen
PublisherIEEE
SubjectCNN-LSTM
Congenital heart defects (CHDs)
deep learning
fetal echocardiogram
hypoplastic left heart syndrome (HLHS)
stacking machine learning
TitleDeep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models from Fetal Echocardiogram: A Pilot Study
TypeArticle
Pagination110375 - 110390
Volume Number11
ESSN2169-3536
dc.accessType Open Access


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