Development of an artificial intelligence powered software for automated analysis of skeletal muscle ultrasonography.

Journal: Scientific reports
PMID: 40301467

Abstract

Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analysis to achieve objective quantification of muscle size and quality. We aimed to develop and validate a software tool powered by artificial intelligence (AI) by measuring its consistency and comparability of expert manual analysis quantifying lower limb muscle ultrasound images. Quadriceps complex (QC) and tibialis anterior (TA) muscle images of healthy, intensive care unit, and/or lung cancer participants were captured with portable devices. Manual analyses of muscle size and quality were performed by experienced physiotherapists taking approximately 24 h to analyze all 180 images, while automated analyses were performed using a custom-built deep-learning model (MyoVision-US), taking 247 s (saving time = 99.8%). Consistency between the manual and automated analyses was good to excellent for all QC (ICC = 0.85-0.99) and TA (ICC = 0.93-0.99) measurements, even for critically ill (ICC = 0.91-0.98) and lung cancer (ICC = 0.85-0.99) images. The comparability of MyoVision-US was moderate to strong for QC (adj. R = 0.56-0.94) and TA parameters (adj. R = 0.81-0.97). The application of AI automating lower limb muscle ultrasound analyses showed excellent consistency and strong comparability compared with human analysis across healthy, acute, and chronic population.

Authors

  • Zoe Calulo Rivera
    Department of Physiotherapy, School of Health Sciences, The University of Melbourne, Melbourne, Australia.
  • Felipe González-Seguel
    Center for Muscle Biology, University of Kentucky, Lexington, KY, USA.
  • Arimitsu Horikawa-Strakovsky
    Center for Muscle Biology, University of Kentucky, Lexington, KY, USA.
  • Catherine Granger
    Department of Physiotherapy, School of Health Sciences, The University of Melbourne, Melbourne, Australia.
  • Aarti Sarwal
    Department of Neurology, Wake Forest Baptist Medical Center, Winston Salem, NC, USA.
  • Sanjay Dhar
    Division of Pulmonary, Critical Care & Sleep Medicine, Department of Internal Medicine, College of Medicine, University of Kentucky, Lexington, KY, USA.
  • George Ntoumenopoulos
    Department of Physiotherapy, St. Vincent's Hospital, Sydney, Australia.
  • Jin Chen
    Department of Neurology, University of Texas Health Science Center at Houston, Houston, TX.
  • V K Cody Bumgardner
    Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA.
  • Selina M Parry
    Department of Physiotherapy, School of Health Sciences, The University of Melbourne, Melbourne, Australia.
  • Kirby P Mayer
    Center for Muscle Biology, University of Kentucky, Lexington, KY, USA. kpmaye2@uky.edu.
  • Yuan Wen
    Nanchang Institute of Technology, Nanchang 33108, China.

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