Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.
Ramin Yousefpour ShahrivarFatemeh KaramiEbrahim KaramiPublished in: Biomimetics (Basel, Switzerland) (2023)
Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.
Keyphrases
- deep learning
- machine learning
- magnetic resonance imaging
- convolutional neural network
- healthcare
- artificial intelligence
- optical coherence tomography
- ultrasound guided
- contrast enhanced ultrasound
- high resolution
- mental health
- loop mediated isothermal amplification
- working memory
- real time pcr
- big data
- magnetic resonance
- label free
- quality improvement
- pain management
- bioinformatics analysis