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Jost, E.* ; Kosian, P.* ; Jimenez Cruz, J.* ; Albarqouni, S. ; Gembruch, U.* ; Strizek, B.* ; Recker, F.*

Evolving the era of 5D ultrasound? A systematic literature review on the applications for artificial intelligence ultrasound imaging in obstetrics and gynecology.

J. Clin. Med. 12:31 (2023)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Korrespondenzautor
Schlagwörter Application ; Artificial Intelligence ; Deep Learning ; Fetal Echocardiography ; Gynecology ; Obstetrics ; Systematic Review ; Ultrasound Imaging; Standard Plane Localization; Fetal Ultrasound; Diagnostic System; Gestational-age; Practice Guidelines; Hiatal Dimensions; Placental Volume; Texture Analysis; Adnexal Masses; Random Forest
ISSN (print) / ISBN 2077-0383
e-ISSN 2077-0383
Zeitschrift Journal of Clinical Medicine
Quellenangaben Band: 12, Heft: 21, Seiten: , Artikelnummer: 31 Supplement: ,
Verlag MDPI
Verlagsort Basel
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Förderungen University of Bonn