Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non‐occiput anterior positions during labor

Author:

Ghi T.1ORCID,Conversano F.2ORCID,Ramirez Zegarra R.13ORCID,Pisani P.2,Dall'Asta A.1ORCID,Lanzone A.4,Lau W.5ORCID,Vimercati A.6,Iliescu D. G.78ORCID,Mappa I.9,Rizzo G.910ORCID,Casciaro S.2,Morello Rocco,Schera Giovanni Battista Luca,Franchini Roberto,Fieni Stefania,Di Trani Maria Giovanna,Pignatelli Daniela,Sirico Angelo,Hung Catherine,DîrĂ Laurențiu,Levy Roni,Vaisbuch Edi,Lees Christoph,Usman Sana,Iurlaro Enrico,Tondo Marta,Winkler Alice,Hassan Wassim A.,Taylor Sasha,Wiafe Yaw Amo,Eggebø Torbjørn Moe,Henrich Wolfgang,Hinkson Larry,Vaso Edvin,

Affiliation:

1. Department of Medicine and Surgery, Obstetrics and Gynecology Unit University of Parma Parma Italy

2. National Research Council Institute of Clinical Physiology Lecce Italy

3. Department of Obstetrics and Gynecology St Joseph Krankenhaus Berlin Germany

4. Obstetrics and High‐Risk Unit Fondazione Policlinico A. Gemelli IRCCS Rome Italy

5. Department of Obstetrics and Gynecology Kwong Wah Hospital Kowloon Hong Kong

6. Department of Obstetrics, Gynecology, Neonatology and Anesthesiology University Hospital of Bari Consorziale Policlinico Bari Italy

7. University Emergency County Hospital Craiova Romania

8. University of Medicine and Pharmacy Craiova Romania

9. Division of Maternal and Fetal Medicine, Cristo Re Hospital University of Rome Tor Vergata Rome Italy

10. Department of Obstetrics and Gynecology The First I.M. Sechenov Moscow State Medical University Moscow Russia

Publisher

Wiley

Subject

Obstetrics and Gynecology,Radiology, Nuclear Medicine and imaging,Reproductive Medicine,General Medicine,Radiological and Ultrasound Technology

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