Advancements in Deep Learning Techniques for Enhanced Assessment of Fetal Anomalies in Prenatal Imaging: Review of Current Applications and Future Directions
Abstract
Background: Deep learning (DL) has emerged as a transformative technology in the field of medical imaging, particularly in prenatal assessments. The application of DL algorithms in fetal imaging aims to address challenges such as human subjectivity and interobserver variability, while enhancing diagnostic accuracy.
Methods: This review synthesizes recent advancements in the application of deep learning techniques for evaluating fetal anomalies. A comprehensive literature search was conducted to gather evidence on the efficacy of DL in various aspects of prenatal imaging, including anatomical assessment, biometric measurements, and the detection of congenital abnormalities.
Results: The findings indicate that deep learning models exhibit superior performance in identifying normal and abnormal fetal anatomy compared to traditional methods. These models effectively classify images, localize anatomical structures, and segment key features, significantly reducing examination times and improving workflow. Furthermore, multiple studies demonstrate that DL can mitigate the impact of human error, achieving classifications that rival or exceed those of experienced sonographers.
Conclusion: The integration of deep learning into prenatal imaging holds considerable promise for enhancing diagnostic capabilities and improving patient outcomes. As these technologies evolve, they offer the potential to support clinicians, particularly in resource-limited settings where access to skilled sonographers is limited. Future research should focus on refining these models and ensuring their clinical applicability to maximize the benefits of deep learning in obstetric care.
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