The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

Jan Weichert; Amrei Welp; Jann Lennard Scharf; Christoph Dracopoulos; Wolf-Henning Becker; Michael Gembicki

doi:10.1055/a-1522-3029

Geburtshilfe und Frauenheilkunde, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Geburtshilfe Frauenheilkd 2021; 81(11): 1203-1216
DOI: 10.1055/a-1522-3029

GebFra Science

Review/Übersicht

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

Artikel in mehreren Sprachen: English | deutsch

Autor*innen

Jan Weichert

¹Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany

²Zentrum für Pränatalmedizin an der Elbe, Hamburg, Germany
Amrei Welp

¹Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
Jann Lennard Scharf

¹Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
Christoph Dracopoulos

¹Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
Wolf-Henning Becker

²Zentrum für Pränatalmedizin an der Elbe, Hamburg, Germany
Michael Gembicki

¹Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany

Abstract

The long-awaited progress in digitalisation is generating huge amounts of medical data every day, and manual analysis and targeted, patient-oriented evaluation of this data is becoming increasingly difficult or even infeasible. This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare system. The utilisation of state-of-the-art equipment and techniques in almost all areas of medicine over the past few years has now indeed enabled automation processes to enter – at least in part – into routine clinical practice. Such systems utilise a wide variety of artificial intelligence (AI) techniques, the majority of which have been developed to optimise medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection and classification and, as an emerging field of research, radiogenomics. Tasks handled by AI are completed significantly faster and more precisely, clearly demonstrated by now in the annual findings of the ImageNet Large-Scale Visual Recognition Challenge (ILSVCR), first conducted in 2015, with error rates well below those of humans. This review article will discuss the potential capabilities and currently available applications of AI in gynaecological-obstetric diagnostics. The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.

Key words

pregnancy - sonography - gynecology - malformation

Volltext

Referenzen

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