| Autor: |
Anjan Gudigar, Raghavendra U., Jyothi Samanth, Akhila Vasudeva, Ashwal A. J., Krishnananda Nayak, Ru-San Tan, Edward J. Ciaccio, Chui Ping Ooi, Prabal Datta Barua, Filippo Molinari, U. Rajendra Acharya |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Informatics, Vol 9, Iss 2, p 34 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2227-9709 |
| DOI: |
10.3390/informatics9020034 |
| Popis: |
The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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