Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects
Artificial Intelligence is revolutionizing prenatal diagnostics by enhancing the accuracy and efficiency of procedures. This review explores AI and machine learning (ML) in the early detection, prediction, and assessment of neural tube defects (NTDs) through prenatal ultrasound imaging. Recent studi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2025.1514447/full |
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| author | Maryam Yeganegi Mahsa Danaei Sepideh Azizi Fatemeh Jayervand Reza Bahrami Seyed Alireza Dastgheib Heewa Rashnavadi Ali Masoudi Amirmasoud Shiri Kazem Aghili Mahood Noorishadkam Hossein Neamatzadeh |
| author_facet | Maryam Yeganegi Mahsa Danaei Sepideh Azizi Fatemeh Jayervand Reza Bahrami Seyed Alireza Dastgheib Heewa Rashnavadi Ali Masoudi Amirmasoud Shiri Kazem Aghili Mahood Noorishadkam Hossein Neamatzadeh |
| author_sort | Maryam Yeganegi |
| collection | DOAJ |
| description | Artificial Intelligence is revolutionizing prenatal diagnostics by enhancing the accuracy and efficiency of procedures. This review explores AI and machine learning (ML) in the early detection, prediction, and assessment of neural tube defects (NTDs) through prenatal ultrasound imaging. Recent studies highlight the effectiveness of AI techniques, such as convolutional neural networks (CNNs) and support vector machines (SVMs), achieving detection accuracy rates of up to 95% across various datasets, including fetal ultrasound images, genetic data, and maternal health records. SVM models have demonstrated 71.50% accuracy on training datasets and 68.57% on testing datasets for NTD classification, while advanced deep learning (DL) methods report patient-level prediction accuracy of 94.5% and an area under the receiver operating characteristic curve (AUROC) of 99.3%. AI integration with genomic analysis has identified key biomarkers associated with NTDs, such as Growth Associated Protein 43 (GAP43) and Glial Fibrillary Acidic Protein (GFAP), with logistic regression models achieving 86.67% accuracy. Current AI-assisted ultrasound technologies have improved diagnostic accuracy, yielding sensitivity and specificity rates of 88.9% and 98.0%, respectively, compared to traditional methods with 81.5% sensitivity and 92.2% specificity. AI systems have also streamlined workflows, reducing median scan times from 19.7 min to 11.4 min, allowing sonographers to prioritize critical patient care. Advancements in DL algorithms, including Oct-U-Net and PAICS, have achieved recall and precision rates of 0.93 and 0.96, respectively, in identifying fetal abnormalities. Moreover, AI's evolving role in genetic research supports personalized NTD prevention strategies and enhances public awareness through AI-generated health messages. In conclusion, the integration of AI in prenatal diagnostics significantly improves the detection and assessment of NTDs, leading to greater accuracy and efficiency in ultrasound imaging. As AI continues to advance, it has the potential to further enhance personalized healthcare strategies and raise public awareness about NTDs, ultimately contributing to better maternal and fetal outcomes. |
| format | Article |
| id | doaj-art-7c891cb78c194884af67275ee6a8e1e2 |
| institution | OA Journals |
| issn | 2296-2360 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj-art-7c891cb78c194884af67275ee6a8e1e22025-08-20T02:13:19ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602025-04-011310.3389/fped.2025.15144471514447Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defectsMaryam Yeganegi0Mahsa Danaei1Sepideh Azizi2Fatemeh Jayervand3Reza Bahrami4Seyed Alireza Dastgheib5Heewa Rashnavadi6Ali Masoudi7Amirmasoud Shiri8Kazem Aghili9Mahood Noorishadkam10Hossein Neamatzadeh11Department of Obstetrics and Gynecology, School of Medicine, Iranshahr University of Medical Sciences, Iranshahr, IranDepartment of Obstetrics and Gynecology, School of Medicine, Iran University of Medical Sciences, Tehran, IranShahid Akbarabadi Clinical Research Development Unit, Iran University of Medical Sciences, Tehran, IranDepartment of Obstetrics and Gynecology, School of Medicine, Iran University of Medical Sciences, Tehran, IranNeonatal Research Center, Shiraz University of Medical Sciences, Shiraz, IranDepartment of Medical Genetics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, IranSchool of Medicine, Tehran University of Medical Sciences, Tehran, IranSchool of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, IranSchool of Medicine, Shiraz University of Medical Sciences, Shiraz, IranDepartment of Radiology, School of Medicine, Shahid Rahnamoun Hospital, Shahid Sadoughi University of Medical Sciences, Yazd, Iran0Mother and Newborn Health Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran0Mother and Newborn Health Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, IranArtificial Intelligence is revolutionizing prenatal diagnostics by enhancing the accuracy and efficiency of procedures. This review explores AI and machine learning (ML) in the early detection, prediction, and assessment of neural tube defects (NTDs) through prenatal ultrasound imaging. Recent studies highlight the effectiveness of AI techniques, such as convolutional neural networks (CNNs) and support vector machines (SVMs), achieving detection accuracy rates of up to 95% across various datasets, including fetal ultrasound images, genetic data, and maternal health records. SVM models have demonstrated 71.50% accuracy on training datasets and 68.57% on testing datasets for NTD classification, while advanced deep learning (DL) methods report patient-level prediction accuracy of 94.5% and an area under the receiver operating characteristic curve (AUROC) of 99.3%. AI integration with genomic analysis has identified key biomarkers associated with NTDs, such as Growth Associated Protein 43 (GAP43) and Glial Fibrillary Acidic Protein (GFAP), with logistic regression models achieving 86.67% accuracy. Current AI-assisted ultrasound technologies have improved diagnostic accuracy, yielding sensitivity and specificity rates of 88.9% and 98.0%, respectively, compared to traditional methods with 81.5% sensitivity and 92.2% specificity. AI systems have also streamlined workflows, reducing median scan times from 19.7 min to 11.4 min, allowing sonographers to prioritize critical patient care. Advancements in DL algorithms, including Oct-U-Net and PAICS, have achieved recall and precision rates of 0.93 and 0.96, respectively, in identifying fetal abnormalities. Moreover, AI's evolving role in genetic research supports personalized NTD prevention strategies and enhances public awareness through AI-generated health messages. In conclusion, the integration of AI in prenatal diagnostics significantly improves the detection and assessment of NTDs, leading to greater accuracy and efficiency in ultrasound imaging. As AI continues to advance, it has the potential to further enhance personalized healthcare strategies and raise public awareness about NTDs, ultimately contributing to better maternal and fetal outcomes.https://www.frontiersin.org/articles/10.3389/fped.2025.1514447/fullartificial intelligenceprenatal diagnosticsmachine learningneural tube defectsultrasound imaging |
| spellingShingle | Maryam Yeganegi Mahsa Danaei Sepideh Azizi Fatemeh Jayervand Reza Bahrami Seyed Alireza Dastgheib Heewa Rashnavadi Ali Masoudi Amirmasoud Shiri Kazem Aghili Mahood Noorishadkam Hossein Neamatzadeh Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects Frontiers in Pediatrics artificial intelligence prenatal diagnostics machine learning neural tube defects ultrasound imaging |
| title | Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects |
| title_full | Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects |
| title_fullStr | Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects |
| title_full_unstemmed | Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects |
| title_short | Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects |
| title_sort | research advancements in the use of artificial intelligence for prenatal diagnosis of neural tube defects |
| topic | artificial intelligence prenatal diagnostics machine learning neural tube defects ultrasound imaging |
| url | https://www.frontiersin.org/articles/10.3389/fped.2025.1514447/full |
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