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Application of Artificial Intelligence to Automate the Reconstruction of Muscle Cross-Sectional Area Obtained by Ultrasound.
DA Silva, Deivid Gomes; DA Silva, Diego Gomes; Angleri, Vitor; Scarpelli, Maíra Camargo; Bergamasco, João Guilherme Almeida; Nóbrega, Sanmy Rocha; Damas, Felipe; Chaves, Talisson Santos; Camargo, Heloisa DE Arruda; Ugrinowitsch, Carlos; Libardi, Cleiton Augusto.
Afiliação
  • DA Silva DG; MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL.
  • DA Silva DG; MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL.
  • Angleri V; MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL.
  • Scarpelli MC; MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL.
  • Bergamasco JGA; MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL.
  • Nóbrega SR; MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL.
  • Damas F; MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL.
  • Chaves TS; MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL.
  • Camargo HA; Department of Computer Science, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL.
  • Libardi CA; MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL.
Med Sci Sports Exerc ; 56(9): 1840-1848, 2024 Sep 01.
Article em En | MEDLINE | ID: mdl-38637954
ABSTRACT

PURPOSE:

Manual reconstruction (MR) of the vastus lateralis (VL) muscle cross-sectional area (CSA) from sequential ultrasound (US) images is accessible, is reproducible, and has concurrent validity with magnetic resonance imaging. However, this technique requires numerous controls and procedures during image acquisition and reconstruction, making it laborious and time-consuming. The aim of this study was to determine the concurrent validity of VL CSA assessments between MR and computer vision-based automated reconstruction (AR) of CSA from sequential images of the VL obtained by US.

METHODS:

The images from each sequence were manually rotated to align the fascia between images and thus visualize the VL CSA. For the AR, an artificial neural network model was utilized to segment areas of interest in the image, such as skin, fascia, deep aponeurosis, and femur. This segmentation was crucial to impose necessary constraints for the main assembly phase. At this stage, an image registration application, combined with differential evolution, was employed to achieve appropriate adjustments between the images. Next, the VL CSA obtained from the MR ( n = 488) and AR ( n = 488) techniques was used to determine their concurrent validity.

RESULTS:

Our findings demonstrated a low coefficient of variation (CV) (1.51%) for AR compared with MR. The Bland-Altman plot showed low bias and close limits of agreement (+1.18 cm 2 , -1.19 cm 2 ), containing more than 95% of the data points.

CONCLUSIONS:

The AR technique is valid compared with MR when measuring VL CSA in a heterogeneous sample.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Imageamento por Ressonância Magnética / Ultrassonografia / Músculo Quadríceps Limite: Adult / Humans / Male Idioma: En Revista: Med Sci Sports Exerc Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Imageamento por Ressonância Magnética / Ultrassonografia / Músculo Quadríceps Limite: Adult / Humans / Male Idioma: En Revista: Med Sci Sports Exerc Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos