Hypoplastic Left Heart Syndrome

White, Brian R.; Goldberg, David J.; Rychik, Jack

doi:10.1002/9781119612858.ch21

Cited by 1 publication

(2 citation statements)

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“…These networks are capable of automatically extracting spatial features of an image. The approach of transfer learning has been successfully incorporated in many applications [3][4][5][6][7], especially where a large dataset can be hard to find. Thus, it opens the opportunity of utilizing a smaller dataset and reduces the time required to develop a deep learning algorithm from scratch [8][9].…”

Section: F Development Of Classification Modelmentioning

confidence: 99%

“…Generally, hyperplasia refers to a condition of delayed or stunted development in which an organ or a part of it remains below its normal size or remains immature [2]. Hypoplastic left heart syndrome (HLHS) is a group of cardiac malformations characterized by underdevelopment of both the aorta and the left heart, resulting in significantly impaired blood flow into the systemic circusslation and inadequate support for the circulation by the left heart [3]. HLHS is a very severe form of CHD characterized by an insufficient and nonviable left ventricle (LV) caused by congenital abnormalities that compromise the LV's ability to perform its prefusion function [4].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study

Rahman,

Al-Ruweidi,

Sumon

et al. 2023

IEEE Access

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Congenital 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.

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