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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. R2 = 0.56–0.94) and TA parameters (adj. R2 = 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.

Document Type

Article

Publication Date

2025

Notes/Citation Information

© The Author(s) 2025

Digital Object Identifier (DOI)

https://doi.org/10.1038/s41598-025-99522-7

Funding Information

The project was supported by the AIMS—Artificial intelligence in Medicine Alliance of the University of Kentucky and by the NIH National Center for Advancing Translational Sciences through grant number UL-1TR001998 to YW, KPM, and SD. KPM and YW were supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institute of Health K23-AR079583 and R00-AR081367, respectively. SMP is supported by the Al and Val Rosenstrauss Fellowship from the Rebecca L. Cooper Foundation.

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