Development of an artificial intelligence-based application for the diagnosis of sarcopenia: a retrospective cohort study using the health examination dataset.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Medical imaging techniques for diagnosing sarcopenia have been extensively investigated. Studies have proposed using the T-score and patient information as key diagnostic factors. However, these techniques have either been time-consuming or have required separate calculation processes after collecting each parameter. To address this gap, we propose an artificial intelligence (AI)-based web application that automates the collection of data, classification of the lumbar spine 3 (L3) slices, segmentation of the subcutaneous fat, visceral fat, and muscle areas in the classified L3 slices, and quantitative analysis of the segmented areas.

Authors

  • Chang-Won Jeong
    Medical Convergence Research Center, Wonkwang University, Iksan, Republic of Korea.
  • Dong-Wook Lim
    STSC Center, Wonkwang University, Iksan, 54538, South Korea.
  • Si-Hyeong Noh
    STSC Center, Wonkwang University, Iksan, 54538, South Korea.
  • Sung Hyun Lee
    Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Chul Park
    Bio-safety Institute, College of Veterinary Medicine, Chonbuk National University, Jeonju, Korea.