Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254250" target="_blank" >RIV/61989100:27240/24:10254250 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s11831-023-10055-6" target="_blank" >https://link.springer.com/article/10.1007/s11831-023-10055-6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11831-023-10055-6" target="_blank" >10.1007/s11831-023-10055-6</a>

Result language
angličtina
Original language name
Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring
Original language description
Electronic fetal monitoring is used to evaluate fetal well-being by assessing fetal heart activity. The signals produced by the fetal heart carry valuable information about fetal health, but due to non-stationarity and present interference, their processing, analysis and interpretation is considered to be very challenging. Therefore, medical technologies equipped with Artificial Intelligence algorithms are rapidly evolving into clinical practice and provide solutions in the key application areas: noise suppression, feature detection and fetal state classification. The use of artificial intelligence and machine learning in the field of electronic fetal monitoring has demonstrated the efficiency and superiority of such techniques compared to conventional algorithms, especially due to their ability to predict, learn and efficiently handle dynamic Big data. Combining multiple algorithms and optimizing them for given purpose enables timely and accurate diagnosis of fetal health state. This review summarizes the currently used algorithms based on artificial intelligence and machine learning in the field of electronic fetal monitoring, outlines its advantages and limitations, as well as future challenges which remain to be solved.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20201 - Electrical and electronic engineering

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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