Enhancing transvenous lead extraction risk prediction: Integrating imaging biomarkers into machine learning models.

Journal: Heart rhythm
PMID:

Abstract

BACKGROUND: Machine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE).

Authors

  • Vishal S Mehta
    Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom. Electronic address: vishal.mehta@kcl.ac.uk.
  • Yingliang Ma
    School of Computing, Electronics and Mathematics, Coventry University, Coventry, UK.
  • Nadeev Wijesuriya
    Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
  • Felicity DeVere
    Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
  • Sandra Howell
    Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
  • Mark K Elliott
    School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Guy's and St Thomas' Hospital, London, UK.
  • Nilanka N Mannkakara
    Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
  • Tatiana Hamakarim
    Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
  • Tom Wong
    Cardiovascular Research Center, Royal Brompton Hospital, London, UK.
  • Hugh O'Brien
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
  • Steven Niederer
    Department of Biomedical Engineering, King's College London, United Kingdom (C.C., S.N.).
  • Reza Razavi
  • Christopher A Rinaldi
    Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.