Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time.

Journal: Prenatal diagnosis
Published Date:

Abstract

OBJECTIVE: Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI-enabled tools.

Authors

  • Jacqueline Matthew
  • Emily Skelton
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Thomas G Day
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Veronika A Zimmer
    Information and Communication Technologies Department, Universitat Pompeu Fabra, Barcelona, Spain. Electronic address: veronika.zimmer@upf.edu.
  • Alberto Gomez
    Ultromics Ltd, Oxford, United Kingdom.
  • Gavin Wheeler
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Nicolas Toussaint
  • Tianrui Liu
    Department of Computing, Imperial College London, London, UK.
  • Samuel Budd
    Department of Computing, Imperial College London, UK. Electronic address: samuel.budd13@imperial.ac.uk.
  • Karen Lloyd
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Robert Wright
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Shujie Deng
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Nooshin Ghavami
    Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
  • Matthew Sinclair
    Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
  • Qingjie Meng
  • Bernhard Kainz
    Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK.
  • Julia A Schnabel
    Division of Imaging Sciences and Biomedical Engineering, King's College London, UK.
  • Daniel Rueckert
    Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK. Electronic address: d.rueckert@imperial.ac.uk.
  • Reza Razavi
  • John Simpson
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Jo Hajnal