Computer-aided assessment for enlarged fetal heart with deep learning model
Summary: Enlarged fetal heart conditions may indicate congenital heart diseases or other complications, making early detection through prenatal ultrasound essential. However, manual assessments by sonographers are often subjective, time-consuming, and inconsistent. This paper proposes a deep learnin...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225005498 |
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| author | Siti Nurmaini Ade Iriani Sapitri Muhammad Taufik Roseno Muhammad Naufal Rachmatullah Putri Mirani Nuswil Bernolian Annisa Darmawahyuni Bambang Tutuko Firdaus Firdaus Anggun Islami Akhiar Wista Arum Rio Bastian |
| author_facet | Siti Nurmaini Ade Iriani Sapitri Muhammad Taufik Roseno Muhammad Naufal Rachmatullah Putri Mirani Nuswil Bernolian Annisa Darmawahyuni Bambang Tutuko Firdaus Firdaus Anggun Islami Akhiar Wista Arum Rio Bastian |
| author_sort | Siti Nurmaini |
| collection | DOAJ |
| description | Summary: Enlarged fetal heart conditions may indicate congenital heart diseases or other complications, making early detection through prenatal ultrasound essential. However, manual assessments by sonographers are often subjective, time-consuming, and inconsistent. This paper proposes a deep learning approach using the You Only Look Once (YOLO) architecture to automate fetal heart enlargement assessment. Using a set of ultrasound videos, YOLOv8 with a CBAM module demonstrated superior performance compared to YOLOv11 with self-attention. Incorporating the ResNeXtBlock—a residual network with cardinality—additionally enhanced accuracy and prediction consistency. The model exhibits strong capability in detecting fetal heart enlargement, offering a reliable computer-aided tool for sonographers during prenatal screenings. Further validation is required to confirm its clinical applicability. By improving early and accurate detection, this approach has the potential to enhance prenatal care, facilitate timely interventions, and contribute to better neonatal health outcomes. |
| format | Article |
| id | doaj-art-e30121f1e9674bb4b16b097041cb7f8a |
| institution | DOAJ |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-e30121f1e9674bb4b16b097041cb7f8a2025-08-20T03:14:08ZengElsevieriScience2589-00422025-05-0128511228810.1016/j.isci.2025.112288Computer-aided assessment for enlarged fetal heart with deep learning modelSiti Nurmaini0Ade Iriani Sapitri1Muhammad Taufik Roseno2Muhammad Naufal Rachmatullah3Putri Mirani4Nuswil Bernolian5Annisa Darmawahyuni6Bambang Tutuko7Firdaus Firdaus8Anggun Islami9Akhiar Wista Arum10Rio Bastian11Intelligent System Research Group, Universitas Sriwijaya, Palembang, Indonesia; Corresponding authorIntelligent System Research Group, Universitas Sriwijaya, Palembang, IndonesiaComputer Science Department, Universitas Sumatera Selatan, Palembang, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang, IndonesiaDepartment of Obstetrics and Gynecology, Fetomaternal Division, Bunda Hospital, Palembang, IndonesiaDepartment of Obstetrics and Gynecology, Fetomaternal Division, Dr. Mohammad Hoesin General Hospital, Palembang, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang, IndonesiaSummary: Enlarged fetal heart conditions may indicate congenital heart diseases or other complications, making early detection through prenatal ultrasound essential. However, manual assessments by sonographers are often subjective, time-consuming, and inconsistent. This paper proposes a deep learning approach using the You Only Look Once (YOLO) architecture to automate fetal heart enlargement assessment. Using a set of ultrasound videos, YOLOv8 with a CBAM module demonstrated superior performance compared to YOLOv11 with self-attention. Incorporating the ResNeXtBlock—a residual network with cardinality—additionally enhanced accuracy and prediction consistency. The model exhibits strong capability in detecting fetal heart enlargement, offering a reliable computer-aided tool for sonographers during prenatal screenings. Further validation is required to confirm its clinical applicability. By improving early and accurate detection, this approach has the potential to enhance prenatal care, facilitate timely interventions, and contribute to better neonatal health outcomes.http://www.sciencedirect.com/science/article/pii/S2589004225005498Machine learningartificial intelligenceCardiovascular medicine |
| spellingShingle | Siti Nurmaini Ade Iriani Sapitri Muhammad Taufik Roseno Muhammad Naufal Rachmatullah Putri Mirani Nuswil Bernolian Annisa Darmawahyuni Bambang Tutuko Firdaus Firdaus Anggun Islami Akhiar Wista Arum Rio Bastian Computer-aided assessment for enlarged fetal heart with deep learning model iScience Machine learning artificial intelligence Cardiovascular medicine |
| title | Computer-aided assessment for enlarged fetal heart with deep learning model |
| title_full | Computer-aided assessment for enlarged fetal heart with deep learning model |
| title_fullStr | Computer-aided assessment for enlarged fetal heart with deep learning model |
| title_full_unstemmed | Computer-aided assessment for enlarged fetal heart with deep learning model |
| title_short | Computer-aided assessment for enlarged fetal heart with deep learning model |
| title_sort | computer aided assessment for enlarged fetal heart with deep learning model |
| topic | Machine learning artificial intelligence Cardiovascular medicine |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225005498 |
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