Aktuelle Aspekte zur künstlichen Intelligenz in der gynäkologischen Diagnostik

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

doi:10.1007/s00129-022-04993-8

Aktuelle Aspekte zur künstlichen Intelligenz in der gynäkologischen Diagnostik

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

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

Klinik für Frauenheilkunde und Geburtshilfe

Abstract

Artificial intelligence (AI) applications have rapidly increased in medicine. In contrast to obstetrics and prenatal diagnostics, AI in gynecology has not yet been applied in clinical practice. However, AI can aid in preoperative differentiation between benign and malignant lesions of the ovaries, in diagnosis of endometriosis, and assist in the differentiation between leiomyomas and leiomyosarcomas or the classification of Müller duct anomalies. In gynecologic oncology, AI has made tremendous progress by improving cervical cancer detection, ovarian cancer imaging, and uterine cancer diagnosis. This review article examines current aspects of AI algorithms in gynecological diagnostics.

Titel in Übersetzung	Current aspects of artificial intelligence in gynecological diagnostics
Originalsprache	Deutsch
Zeitschrift	Gynakologie
Jahrgang	55
Ausgabenummer	10
Seiten (von - bis)	759-770
Seitenumfang	12
ISSN	2731-7102
DOIs	https://doi.org/10.1007/s00129-022-04993-8
Publikationsstatus	Veröffentlicht - 10.2022

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SDG 3 – Gesundheit und Wohlergehen
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SDG 10 – Weniger Ungleichheiten

Strategische Forschungsbereiche und Zentren

Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)

DFG-Fachsystematik

2.22-21 Gynäkologie und Geburtshilfe
2.22-07 Medizininformatik und medizinische Bioinformatik

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10.1007/s00129-022-04993-8

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title = "Aktuelle Aspekte zur k{\"u}nstlichen Intelligenz in der gyn{\"a}kologischen Diagnostik",

abstract = "Artificial intelligence (AI) applications have rapidly increased in medicine. In contrast to obstetrics and prenatal diagnostics, AI in gynecology has not yet been applied in clinical practice. However, AI can aid in preoperative differentiation between benign and malignant lesions of the ovaries, in diagnosis of endometriosis, and assist in the differentiation between leiomyomas and leiomyosarcomas or the classification of M{\"u}ller duct anomalies. In gynecologic oncology, AI has made tremendous progress by improving cervical cancer detection, ovarian cancer imaging, and uterine cancer diagnosis. This review article examines current aspects of AI algorithms in gynecological diagnostics.",

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

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.",

year = "2022",

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doi = "10.1007/s00129-022-04993-8",

language = "Deutsch",

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AU - Scharf, Jann Lennard

AU - Dracopoulos, Christoph

AU - Gembicki, Michael

AU - Welp, Amrei

AU - Weichert, Jan

PY - 2022/10

Y1 - 2022/10

N2 - Artificial intelligence (AI) applications have rapidly increased in medicine. In contrast to obstetrics and prenatal diagnostics, AI in gynecology has not yet been applied in clinical practice. However, AI can aid in preoperative differentiation between benign and malignant lesions of the ovaries, in diagnosis of endometriosis, and assist in the differentiation between leiomyomas and leiomyosarcomas or the classification of Müller duct anomalies. In gynecologic oncology, AI has made tremendous progress by improving cervical cancer detection, ovarian cancer imaging, and uterine cancer diagnosis. This review article examines current aspects of AI algorithms in gynecological diagnostics.

AB - Artificial intelligence (AI) applications have rapidly increased in medicine. In contrast to obstetrics and prenatal diagnostics, AI in gynecology has not yet been applied in clinical practice. However, AI can aid in preoperative differentiation between benign and malignant lesions of the ovaries, in diagnosis of endometriosis, and assist in the differentiation between leiomyomas and leiomyosarcomas or the classification of Müller duct anomalies. In gynecologic oncology, AI has made tremendous progress by improving cervical cancer detection, ovarian cancer imaging, and uterine cancer diagnosis. This review article examines current aspects of AI algorithms in gynecological diagnostics.

UR - https://www.scopus.com/pages/publications/85150439669

U2 - 10.1007/s00129-022-04993-8

DO - 10.1007/s00129-022-04993-8

M3 - Übersichtsarbeiten

AN - SCOPUS:85150439669

SN - 2731-7102

VL - 55

SP - 759

EP - 770

JO - Gynakologie

JF - Gynakologie

IS - 10

ER -

Aktuelle Aspekte zur künstlichen Intelligenz in der gynäkologischen Diagnostik

Abstract

UN SDGs

Strategische Forschungsbereiche und Zentren

DFG-Fachsystematik

Dokumente

Weitere Links

Fingerprint

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