Research Advancements in the Use of Artificial Intelligence for Prenatal Diagnosis of Neural Tube Defects

Maryam Yeganegi ¹ Mahsa Danaie ^2* Sepideh Azizi ² Fatemeh Jayervand ² Reza Bahrami ³ Seyed Alireza Dastgheib ³ Heewa Rashnavadi ⁴ Ali Masoudi ⁵ Amirmasoud Shiri ³ Kazem Aghili ⁵ Mahood Noorishadkam ⁵ Hossein Neamatzadeh ⁵

• ¹ Iranshahr University of Medical Sciences, Iranshahr, Sistan and Baluchestan, Iran
• ² Iran University of Medical Sciences, Tehran, Iran
• ³ Shiraz University of Medical Sciences, Shiraz, Fars, Iran
• ⁴ Tehran University of Medical Sciences, Tehran, Tehran, Iran
• ⁵ Shahid Sadoughi University of Medical Sciences and Health Services, Yazd, Yazd, Iran

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 minutes to 11.4 minutes, 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 AIgenerated 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.