Interaction between clinicians and artificial intelligence to detect fetal atrioventricular septal defects on ultrasound: how can we optimize collaborative performance?

T G Day^*, J Matthew, S F Budd, L Venturini, R Wright, A Farruggia, T V Vigneswaran, V Zidere, J V Hajnal, R Razavi, J M Simpson, B Kainz

^*Corresponding author for this work

School of Biomedical Engineering and Imaging Sciences
Faculty of Life Sciences and Medicine
1Department of Hepatology, Imperial College London, London, UK. 2Institute of Liver Studies at King's College School of Medicine at King's College Hospital, London, UK. 3Institute of Human Health and Performance, University College London, London, UK. 4Department of Asthma Allergy and Lung Biology, King's College London, London, UK. 5Division of Critical Care Medicine, University of Alberta, Edmonton, Canada.
Adult Congenital Heart Disease Department
Evelina London Children's Healthcare
Royal Brompton and Harefield Hospitals NHS Trust (Part of Guy's and St Thomas' NHS Foundation Trust), Sydney Street, London, UK.
Harris Birthright Centre
The Lundbeck Foundation; Aarhus University; King's College London; South London and Maudsley NHS Foundation Trust
Department of Artificial Intelligence in Biomedical Engineering
Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Department of Computing
Department of Materials, Imperial College London, London SW7 2AZ, United Kingdom; Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom; Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, United Kingdom.

Research output: Contribution to journal › Article › peer-review

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