Nina Montik

Azienda Ospedaliero-Universitaria

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Scholarly Papers (1)

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AI-Driven Ultrasound Detection of Ovarian Cancer that Generalizes: An International Multicentre Validation Study

Number of pages: 33 Posted: 22 Jan 2024
Södersjukhuset (Sös) - Department of Clinical Sciences and Education, Royal Institute of Technology (KTH) - School of Electrical Engineering and Computer Science, Södersjukhuset (Sös) - Department of Clinical Sciences and Education, Södersjukhuset (Sös) - Department of Clinical Sciences and Education, Royal Institute of Technology (KTH) - School of Electrical Engineering and Computer Science, Medical University of Lublin, University of Milan - DIBIC “Luigi Sacco”, Università degli Studi di Milano-Bicocca, Institute for the Care of Mother and Child, Lithuanian University of Health Sciences, European Institute of Oncology (IEO) IRCCS, Charles University in Prague - Department of Obstetrics and Gynecology, Fondazione Poliambulanza Istituto Ospedaliero, Forlì and Faenza Hospitals AUSL - Obstetrics and Gynecology Unit, Hospital Universitario Dexeus - Department of Obstetrics, Gynecology, and Reproduction, Medical University of Silesia, Universita di Cagliari, IRCCS “Burlo Garofolo” - Institute for Maternal and Child Health, Skåne University Hospital - Department of Obstetrics and Gynecology, Azienda Ospedaliero-Universitaria, University of Navarra - Clínica Universidad de Navarra, National and Kapodistrian University of Athens, Rizal Medical Center, University of Milan - DIBIC “Luigi Sacco”, Morgagni-Pierantoni Hospital, Charles University, Mater Olbia Hospital - Gynecology and Breast Care Center, Fondazione Poliambulanza Istituto Ospedaliero, Royal Institute of Technology (KTH), Royal Institute of Technology (KTH) - School of Electrical Engineering and Computer Science and Karolinska Institutet
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Abstract:

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ovarian neoplasms, ultrasonography, diagnostic imaging, artificial intelligence, deep learning, machine learning, triage, clinical decision-making