Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review.

Journal: BMC bioinformatics
Published Date: Oct 17, 2022

Abstract

BACKGROUND: Predicting morphological changes to anatomical structures from 3D shapes such as blood vessels or appearance of the face is a growing interest to clinicians. Machine learning (ML) has had great success driving predictions in 2D, however, methods suitable for 3D shapes are unclear and the use cases unknown.

Authors

  • Joyce Zhanzi Wang
    School of Health Sciences, Faculty of Medicine and Health & Children's Hospital at Westmead, University of Sydney, Sydney, NSW, 2006, Australia. joyce.wang@sydney.edu.au.
  • Jonathon Lillia
    EPIC Lab, Kids Research, The Children's Hospital at Westmead, Locked Bag 4001, Westmead, Sydney, NSW, 2145, Australia.
  • Ashnil Kumar
    School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia. Electronic address: ashnil.kumar@sydney.edu.au.
  • Paula Bray
    School of Health Sciences, Faculty of Medicine and Health & Children's Hospital at Westmead, University of Sydney, Sydney, NSW, 2006, Australia.
  • Jinman Kim
    School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia. Electronic address: jinman.kim@sydney.edu.au.
  • Joshua Burns
    School of Health Sciences, Faculty of Medicine and Health & Children's Hospital at Westmead, University of Sydney, Sydney, NSW, 2006, Australia.
  • Tegan L Cheng
    School of Health Sciences, Faculty of Medicine and Health & Children's Hospital at Westmead, University of Sydney, Sydney, NSW, 2006, Australia.

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