Zoe Calulo Rivera, Felipe González-Seguel, Arimitsu Horikawa-Strakovsky, Catherine Granger, Aarti Sarwal, Sanjay Dhar, George Ntoumenopoulos, Jin Chen, V. K. Cody Bumgardner, Selina M. Parry, Kirby P. Mayer, Yuan Wen
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Abstract
Introduction/Aims 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 morphometry. This study aimed to develop and validate a software tool powered by artificial intelligence (AI) by measuring its consistency and predictability of expert manual analysis quantifying lower limb muscle ultrasound images across healthy, acute, and chronic illness subjects.
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