Application of Artificial Intelligence in Improving the Accuracy of Fetal Ultrasound

Background and Objective: Ultrasound imaging is a vital tool in prenatal care, enabling the assessment of fetal growth and anatomy. However, its diagnostic accuracy is highly dependent on the operator’s experience and image interpretation skills. This study investigates the potential of artificial intelligence (AI) to enhance the accuracy and reliability of fetal ultrasound image analysis for the detection of structural and functional anomalies.
Methods: A deep learning model based on a convolutional neural network (CNN) was developed to automatically analyze fetal ultrasound images. A dataset of 1,200 labeled images obtained during the second trimester was used for training and validation. The AI system’s performance was compared with traditional manual interpretation and clinical expert assessments.
Key Findings: The developed AI-based system improved diagnostic accuracy by up to 25% compared to conventional methods and significantly reduced inter-observer variability. The proposed model achieved over 93% accuracy in detecting fetal cardiac and cerebral anomalies.
Conclusion: Artificial intelligence can serve as a powerful diagnostic assistant, enhancing the reliability, precision, and efficiency of fetal ultrasound examinations. The integration of AI in prenatal imaging represents a significant step toward standardized, data-driven, and intelligent prenatal diagnostics.