Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches
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, de...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/7/519 |
| _version_ | 1827640466746638336 |
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| author | Ramin Yousefpour Shahrivar Fatemeh Karami Ebrahim Karami |
| author_facet | Ramin Yousefpour Shahrivar Fatemeh Karami Ebrahim Karami |
| author_sort | Ramin Yousefpour Shahrivar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:59:49Z |
| format | Article |
| id | doaj.art-e7badac1898f4bc4901c7f2abbdd20f9 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-09T16:59:49Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-e7badac1898f4bc4901c7f2abbdd20f92023-11-24T14:31:34ZengMDPI AGBiomimetics2313-76732023-11-018751910.3390/biomimetics8070519Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based ApproachesRamin Yousefpour Shahrivar0Fatemeh Karami1Ebrahim Karami2Department of Biology, College of Convergent Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, 14515-775, IranDepartment of Medical Genetics, Applied Biophotonics Research Center, Science and Research Branch, Islamic Azad University, Tehran, 14515-775, IranDepartment of Engineering and Applied Sciences, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, CanadaFetal 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.https://www.mdpi.com/2313-7673/8/7/519fetal anomalyprenatal diagnosismachine learningdeep learningultrasonography imaging |
| spellingShingle | Ramin Yousefpour Shahrivar Fatemeh Karami Ebrahim Karami Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches Biomimetics fetal anomaly prenatal diagnosis machine learning deep learning ultrasonography imaging |
| title | Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches |
| title_full | Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches |
| title_fullStr | Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches |
| title_full_unstemmed | Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches |
| title_short | Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches |
| title_sort | enhancing fetal anomaly detection in ultrasonography images a review of machine learning based approaches |
| topic | fetal anomaly prenatal diagnosis machine learning deep learning ultrasonography imaging |
| url | https://www.mdpi.com/2313-7673/8/7/519 |
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