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

Translated title of the contribution: Artificial intelligence in prenatal cardiac diagnostics

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

^*Corresponding author for this work

4 Citations (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.

Translated title of the contribution	Artificial intelligence in prenatal cardiac diagnostics
Original language	German
Journal	Gynakologe
Volume	55
Issue number	1
Pages (from-to)	22-31
Number of pages	10
ISSN	0017-5994
DOIs	https://doi.org/10.1007/s00129-021-04890-6
Publication status	Published - 01.2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

2.22-21 Gynaecology and Obstetrics
2.22-20 Pediatric and Adolescent Medicine
4.43-04 Artificial Intelligence and Machine Learning Methods
2.22-12 Cardiology, Angiology

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10.1007/s00129-021-04890-6

Cite this

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title = "K{\"u}nstliche Intelligenz in der pr{\"a}natalen kardialen Diagnostik",

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.",

author = "Jan Weichert and Amrei Welp and Scharf, \{Jann Lennard\} and Christoph Dracopoulos and Achim Rody and Michael Gembicki",

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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.

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DO - 10.1007/s00129-021-04890-6

M3 - Übersichtsarbeiten

AN - SCOPUS:85121564889

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JF - Gynakologe

IS - 1

ER -

Künstliche Intelligenz in der pränatalen kardialen Diagnostik

Abstract

UN SDGs

Research Areas and Centers

DFG Research Classification Scheme

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