Artificial Intelligence for the Prenatal Ultrasound Diagnosis of Congenital Heart Disease: A Narrative Review
Objective: Congenital heart disease (CHD) is the most prevalent congenital anomaly, imposing a significant burden in morbidity and mortality. Recent advances in artificial intelligence (AI) have introduced numerous new tools to fetal cardiac ultrasound, including automated generat...
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| Format: | Article |
| Language: | English |
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IMR Press
2024-11-01
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| Series: | Clinical and Experimental Obstetrics & Gynecology |
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| Online Access: | https://www.imrpress.com/journal/CEOG/51/11/10.31083/j.ceog5111244 |
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| author | Arianna Riva Mariachiara Guerra Stefania Di Gangi Paola Veronese Vladimiro L Vida |
| author_facet | Arianna Riva Mariachiara Guerra Stefania Di Gangi Paola Veronese Vladimiro L Vida |
| author_sort | Arianna Riva |
| collection | DOAJ |
| description | Objective: Congenital heart disease (CHD) is the most prevalent congenital anomaly, imposing a significant burden in morbidity and mortality. Recent advances in artificial intelligence (AI) have introduced numerous new tools to fetal cardiac ultrasound, including automated generation of fetal cardiac planes and identification of specific CHD diagnostic views. Mechanism: Through a narrative review of literature, we described AI technology on automated CHD detection, lesion identification, and associated challenges, such as training datasets and image segmentation. Findings in Brief: The search provided 28 eligible studies. Conclusions: Artificial intelligence seems to be a promising tool to help clinicians in daily clinical activity: it can automate the detection of standard cardiac planes and assist in identifying abnormalities. The main advantages that emerged from this review are related to the reduction of inter- and intra-operator variability, improvement of overall diagnostic performance and accuracy. However, nowadays, its integration into routine clinical practice gives rise to several issues. |
| format | Article |
| id | doaj-art-488d680325f4499bb91b0abd9ab33d80 |
| institution | Kabale University |
| issn | 0390-6663 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Clinical and Experimental Obstetrics & Gynecology |
| spelling | doaj-art-488d680325f4499bb91b0abd9ab33d802025-08-20T03:36:01ZengIMR PressClinical and Experimental Obstetrics & Gynecology0390-66632024-11-01511124410.31083/j.ceog5111244S0390-6663(24)02474-6Artificial Intelligence for the Prenatal Ultrasound Diagnosis of Congenital Heart Disease: A Narrative ReviewArianna Riva0Mariachiara Guerra1Stefania Di Gangi2Paola Veronese3Vladimiro L Vida4Department of Women’s and Children’s Health, Maternal Fetal Medicine Unit, University of Padua, 35128 Padua, ItalyDepartment of Women’s and Children’s Health, Maternal Fetal Medicine Unit, University of Padua, 35128 Padua, ItalyDepartment of Women’s and Children’s Health, Maternal Fetal Medicine Unit, University of Padua, 35128 Padua, ItalyDepartment of Women’s and Children’s Health, Maternal Fetal Medicine Unit, University of Padua, 35128 Padua, ItalyDepartment of Cardiac, Thoracic, Vascular Sciences and Public Health, Pediatric Cardiac Surgery Unit, University of Padua, 35128 Padua, ItalyObjective: Congenital heart disease (CHD) is the most prevalent congenital anomaly, imposing a significant burden in morbidity and mortality. Recent advances in artificial intelligence (AI) have introduced numerous new tools to fetal cardiac ultrasound, including automated generation of fetal cardiac planes and identification of specific CHD diagnostic views. Mechanism: Through a narrative review of literature, we described AI technology on automated CHD detection, lesion identification, and associated challenges, such as training datasets and image segmentation. Findings in Brief: The search provided 28 eligible studies. Conclusions: Artificial intelligence seems to be a promising tool to help clinicians in daily clinical activity: it can automate the detection of standard cardiac planes and assist in identifying abnormalities. The main advantages that emerged from this review are related to the reduction of inter- and intra-operator variability, improvement of overall diagnostic performance and accuracy. However, nowadays, its integration into routine clinical practice gives rise to several issues.https://www.imrpress.com/journal/CEOG/51/11/10.31083/j.ceog5111244artificial intelligence (ai)congenital heart disease (chd)fetal echocardiographyprenatal ultrasound |
| spellingShingle | Arianna Riva Mariachiara Guerra Stefania Di Gangi Paola Veronese Vladimiro L Vida Artificial Intelligence for the Prenatal Ultrasound Diagnosis of Congenital Heart Disease: A Narrative Review Clinical and Experimental Obstetrics & Gynecology artificial intelligence (ai) congenital heart disease (chd) fetal echocardiography prenatal ultrasound |
| title | Artificial Intelligence for the Prenatal Ultrasound Diagnosis of Congenital Heart Disease: A Narrative Review |
| title_full | Artificial Intelligence for the Prenatal Ultrasound Diagnosis of Congenital Heart Disease: A Narrative Review |
| title_fullStr | Artificial Intelligence for the Prenatal Ultrasound Diagnosis of Congenital Heart Disease: A Narrative Review |
| title_full_unstemmed | Artificial Intelligence for the Prenatal Ultrasound Diagnosis of Congenital Heart Disease: A Narrative Review |
| title_short | Artificial Intelligence for the Prenatal Ultrasound Diagnosis of Congenital Heart Disease: A Narrative Review |
| title_sort | artificial intelligence for the prenatal ultrasound diagnosis of congenital heart disease a narrative review |
| topic | artificial intelligence (ai) congenital heart disease (chd) fetal echocardiography prenatal ultrasound |
| url | https://www.imrpress.com/journal/CEOG/51/11/10.31083/j.ceog5111244 |
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