Künstliche Intelligenz in der pränatalen kardialen Diagnostik

Jan Weichert; Amrei Welp; Jann Lennard Scharf; Christoph Dracopoulos; Achim Rody; Michael Gembicki

doi:10.1007/s00129-021-04890-6

Künstliche Intelligenz in der pränatalen kardialen Diagnostik

Jan Weichert^*, Amrei Welp, Jann Lennard Scharf, Christoph Dracopoulos, Achim Rody, Michael Gembicki

^*Korrespondierende/r Autor/-in für diese Arbeit

4 Zitate (Scopus)

Abstract

The prenatal detection rates of fetal heart defects have remained low despite the implementation of national and international screening programs. The detection rates in the low-risk population range from 22.5% to 52.8%. Promising approaches for improved detection rates could include automated applications of artificial intelligence (AI). With reference to novel and already established AI solutions from adult cardiology, this review discusses the possibilities and limitations of AI algorithms for fetal echocardiography.

Titel in Übersetzung	Artificial intelligence in prenatal cardiac diagnostics
Originalsprache	Deutsch
Zeitschrift	Gynakologe
Jahrgang	55
Ausgabenummer	1
Seiten (von - bis)	22-31
Seitenumfang	10
ISSN	0017-5994
DOIs	https://doi.org/10.1007/s00129-021-04890-6
Publikationsstatus	Veröffentlicht - 01.2022

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 3 – Gesundheit und Wohlergehen

Strategische Forschungsbereiche und Zentren

Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)

DFG-Fachsystematik

2.22-21 Gynäkologie und Geburtshilfe
2.22-20 Kinder- und Jugendmedizin
4.43-04 Künstliche Intelligenz und Maschinelles Lernverfahren
2.22-12 Kardiologie, Angiologie

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10.1007/s00129-021-04890-6

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abstract = "The prenatal detection rates of fetal heart defects have remained low despite the implementation of national and international screening programs. The detection rates in the low-risk population range from 22.5\% to 52.8\%. Promising approaches for improved detection rates could include automated applications of artificial intelligence (AI). With reference to novel and already established AI solutions from adult cardiology, this review discusses the possibilities and limitations of AI algorithms for fetal echocardiography.",

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doi = "10.1007/s00129-021-04890-6",

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AU - Weichert, Jan

AU - Welp, Amrei

AU - Scharf, Jann Lennard

AU - Dracopoulos, Christoph

AU - Rody, Achim

AU - Gembicki, Michael

PY - 2022/1

Y1 - 2022/1

N2 - The prenatal detection rates of fetal heart defects have remained low despite the implementation of national and international screening programs. The detection rates in the low-risk population range from 22.5% to 52.8%. Promising approaches for improved detection rates could include automated applications of artificial intelligence (AI). With reference to novel and already established AI solutions from adult cardiology, this review discusses the possibilities and limitations of AI algorithms for fetal echocardiography.

AB - The prenatal detection rates of fetal heart defects have remained low despite the implementation of national and international screening programs. The detection rates in the low-risk population range from 22.5% to 52.8%. Promising approaches for improved detection rates could include automated applications of artificial intelligence (AI). With reference to novel and already established AI solutions from adult cardiology, this review discusses the possibilities and limitations of AI algorithms for fetal echocardiography.

UR - https://www.scopus.com/pages/publications/85121564889

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U2 - 10.1007/s00129-021-04890-6

DO - 10.1007/s00129-021-04890-6

M3 - Übersichtsarbeiten

AN - SCOPUS:85121564889

SN - 0017-5994

VL - 55

SP - 22

EP - 31

JO - Gynakologe

JF - Gynakologe

IS - 1

ER -

Künstliche Intelligenz in der pränatalen kardialen Diagnostik

Abstract

UN SDGs

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