Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Archives of Computational Methods in Engineering

ISSN

1134-3060

e-ISSN

1886-1784

Svazek periodika

1

Číslo periodika v rámci svazku

31 January 2024

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

32

Strana od-do

1-32

Kód UT WoS článku

001152700200001

EID výsledku v databázi Scopus

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