Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study
<p dir="ltr">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 be...
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2023
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| _version_ | 1864513527405871104 |
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| author | Tawsifur Rahman (14150523) |
| author2 | Mahmoud Khatib A. A. Al-Ruweidi (12535527) Md. Shaheenur Islam Sumon (17983810) Reema Yousef Kamal (17983813) Muhammad E. H. Chowdhury (14150526) Huseyin C. Yalcin (6695099) |
| author2_role | author author author author author |
| author_facet | Tawsifur Rahman (14150523) Mahmoud Khatib A. A. Al-Ruweidi (12535527) Md. Shaheenur Islam Sumon (17983810) Reema Yousef Kamal (17983813) Muhammad E. H. Chowdhury (14150526) Huseyin C. Yalcin (6695099) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tawsifur Rahman (14150523) Mahmoud Khatib A. A. Al-Ruweidi (12535527) Md. Shaheenur Islam Sumon (17983810) Reema Yousef Kamal (17983813) Muhammad E. H. Chowdhury (14150526) Huseyin C. Yalcin (6695099) |
| dc.date.none.fl_str_mv | 2023-09-18T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3316719 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_Learning_Technique_for_Congenital_Heart_Disease_Detection_Using_Stacking-Based_CNN-LSTM_Models_From_Fetal_Echocardiogram_A_Pilot_Study/25239523 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Heart Echocardiography Videos Diseases Ultrasonic imaging Deep learning Convolutional neural networks Cardiovascular diseases Fetal heart rate Machine learning Congenital heart defects (CHDs) fetal echocardiogram CNN-LSTM stacking machine learning hypoplastic left heart syndrome (HLHS) |
| dc.title.none.fl_str_mv | Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3316719" target="_blank">https://dx.doi.org/10.1109/access.2023.3316719</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3a1c37aa62c7fad636e4ab4e16c99252 |
| identifier_str_mv | 10.1109/access.2023.3316719 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25239523 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot StudyTawsifur Rahman (14150523)Mahmoud Khatib A. A. Al-Ruweidi (12535527)Md. Shaheenur Islam Sumon (17983810)Reema Yousef Kamal (17983813)Muhammad E. H. Chowdhury (14150526)Huseyin C. Yalcin (6695099)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringHeartEchocardiographyVideosDiseasesUltrasonic imagingDeep learningConvolutional neural networksCardiovascular diseasesFetal heart rateMachine learningCongenital heart defects (CHDs)fetal echocardiogramCNN-LSTMstacking machine learninghypoplastic left heart syndrome (HLHS)<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3316719" target="_blank">https://dx.doi.org/10.1109/access.2023.3316719</a></p>2023-09-18T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3316719https://figshare.com/articles/journal_contribution/Deep_Learning_Technique_for_Congenital_Heart_Disease_Detection_Using_Stacking-Based_CNN-LSTM_Models_From_Fetal_Echocardiogram_A_Pilot_Study/25239523CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252395232023-09-18T06:00:00Z |
| spellingShingle | Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study Tawsifur Rahman (14150523) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Heart Echocardiography Videos Diseases Ultrasonic imaging Deep learning Convolutional neural networks Cardiovascular diseases Fetal heart rate Machine learning Congenital heart defects (CHDs) fetal echocardiogram CNN-LSTM stacking machine learning hypoplastic left heart syndrome (HLHS) |
| status_str | publishedVersion |
| title | Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study |
| title_full | Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study |
| title_fullStr | Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study |
| title_full_unstemmed | Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study |
| title_short | Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study |
| title_sort | Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Heart Echocardiography Videos Diseases Ultrasonic imaging Deep learning Convolutional neural networks Cardiovascular diseases Fetal heart rate Machine learning Congenital heart defects (CHDs) fetal echocardiogram CNN-LSTM stacking machine learning hypoplastic left heart syndrome (HLHS) |