Ultrasonography as a method of characterizing abdominal fat tissue

• Autor(a) principal: Ribeiro, Ricardo Teresa
• Data de Publicação: 2022
• Outros Autores: Leitão, Daniel, Dinis, Luís, Ferreira, Aida
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• DOI: 10.25758/set.2213
• Texto Completo: https://doi.org/10.25758/set.2213
• topic: Ultrassonografia; Obesidade; Índice de massa corporal; Tecido adiposo subcutâneo; Ultrasonography; Obesity; Body Mass Index; Subcutaneous adipose tissue

Aim of the study – To compare the thickness of subcutaneous, preperitoneal, and visceral adipose tissue measured by ultrasonography (US) and relate them to the value of Body Mass Index (BMI). Methods – Weight, height, and the abdominal perimeter were determined in 218 volunteers (177 females and 41 males, aged between 18 and 33 years, with a body mass index between 20.03 and 37.27kg/m2), later submitted to abdominal ultrasonography. Further, four lifestyle questions were answered by the volunteers. Results – The US allowed to quantify and classify objectively and reproducibly subcutaneous adipose tissue, preperitoneal and visceral, for p<0.01. Pearson's correlation (p<0.01) did not show inter-observer variability in US measurements of subcutaneous adipose tissue (r=0.9871), preperitoneal (r=0.9003), and visceral (r=0.9407). A strong linear correlation between BMI with subcutaneous adipose tissue (r=0.64) and with preperitoneal (r=0.56) was identified. It was verified that the US could classify the genus based on the thickness of the intra-abdominal adipose tissue, abdominal perimeter, and BMI with a total accuracy of 86.69%. Conclusions – US shows an objective and capable method in the characterization and differentiation of intra-abdominal adipose tissue. The combined use of biometrics (except weight and height) and US data allows a correct estimation of BMI. Future studies are needed to understand the usefulness of the Deep Learning frameworks in automatically detecting different types of abdominal adipose tissue, thus guaranteeing the possibility of the US becoming a quick and preventive method for assessing obesity.