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Machine Learning in Body Composition Analysis.

Michelle I Higgins1, J Peter Marquardt2, Viraj A Master1

  • 1Department of Urology, Emory University School of Medicine, Atlanta, GA, USA.

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|March 27, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) automates body composition analysis (BCA) from medical scans, improving prognostication and treatment for urologic conditions. This technology offers rapid, accurate insights into muscle and fat mass, aiding clinical decisions.

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Area of Science:

  • Urology
  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Body composition analysis (BCA) provides objective anthropometric data crucial for prognostication and treatment planning in urologic conditions.
  • Muscle and adipose tissue mass are key indicators, typically assessed via segmentation of CT or MRI scans.
  • Current semi-automated segmentation methods are time-consuming, hindering clinical integration.

Purpose of the Study:

  • To provide a clinically focused review of machine learning (ML) applications in BCA.
  • To explore the potential of ML to automate and scale BCA for clinical use.
  • To discuss limitations and future directions for ML-driven BCA in urology.

Main Methods:

  • Review of existing literature on ML, specifically convolutional neural networks, for BCA.
  • Analysis of how ML algorithms process cross-sectional imaging data (CT, MRI).
  • Clinical perspective on the translation of ML-based BCA into practice.

Main Results:

  • ML, particularly convolutional neural networks, shows promise for automating BCA.
  • Automated BCA can provide rapid and accurate measurements of muscle and adipose tissue.
  • This facilitates objective data for clinical decision-making.

Conclusions:

  • Machine learning offers a scalable solution to automate time-intensive BCA.
  • ML-powered BCA can enhance clinical decision-making in urology by providing objective body composition data.
  • Further research is needed to address limitations and facilitate clinical translation.