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

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.

Methods

We developed an automated lumbar spine slice classification model using the CNN (EfficientNetV2) algorithm and an automated

SUBMITTER: Jeong CW 

PROVIDER: S-EPMC11796039 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature