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Advisor(s)
Abstract(s)
Objetivo – Comparar a espessura do tecido adiposo subcutâneo, pré-peritoneal e visceral medida por ultrassonografia (US) e relacioná-la com o valor do Índice de Massa Corporal (IMC). Métodos – Duzentos e dezoito voluntários (177 do género feminino e 41 do masculino, entre os 18 e os 33 anos de idade e IMC entre 20,03 e 37,27kg/m2) foram submetidos a uma avaliação antropométrica (peso, altura, perímetro abdominal e questões sobre o estilo de vida) e a uma ultrassonografia abdominal. Resultados – A US permitiu quantificar e classificar de forma objetiva e reprodutível o tecido adiposo subcutâneo, pré-peritoneal e visceral, para p<0,01. A correlação de Pearson (com p<0,01) não evidenciou variabilidade interobservador nas medições por US do tecido adiposo subcutâneo (r=0,9871), pré-peritoneal (r=0,9003) e visceral (r=0,9407). Identificou-se uma correlação linear forte entre o IMC com o tecido adiposo subcutâneo (r=0,64) e uma correlação moderada com o pré-peritoneal (r=0,56). Verificou-se que a US consegue classificar o género (masculino/feminino) com base nas espessuras do tecido adiposo intra-abdominal, perímetro abdominal e IMC com uma exatidão total de 86,69%. Conclusões – A US demonstra ser um método objetivo e capaz na caracterização e diferenciação do tecido adiposo intra-abdominal. A utilização combinada de dados demográficos (excepto peso e altura) e US permite uma correta estimativa do IMC. Estudos futuros são necessários para se perceber a utilidade das frameworks de Deep Learning na deteção automática dos diferentes tipos de tecido adiposo abdominal, garantindo assim a possibilidade de a US se tornar um método preventivo e rápido para avaliação da obesidade.
ABSTRACT: 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 can 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 to be an objective and capable method in the characterization and differentiation of intra-abdominal adipose tissue. The combined use of biometric (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 the automatic detection of different types of abdominal adipose tissue, thus guaranteeing the possibility of the US becoming a quick and preventive method for assessing obesity.
ABSTRACT: 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 can 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 to be an objective and capable method in the characterization and differentiation of intra-abdominal adipose tissue. The combined use of biometric (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 the automatic detection of different types of abdominal adipose tissue, thus guaranteeing the possibility of the US becoming a quick and preventive method for assessing obesity.
Description
Keywords
Radiologia Ultrassonografia Obesidade Índice de massa corporal IMC Tecido adiposo subcutâneo Radiology Ultrasonography Obesity Body mass Index BMI Subcutaneous adipose tissue
Citation
Ribeiro R, Leitão D, Dinis L, Ferreira AP. A ultrassonografia enquanto método para caracterização do tecido adiposo abdominal. Saúde & Tecnologia. 2019;(22):13-21.
Publisher
Instituto Politécnico de Lisboa, Escola Superior de Tecnologia da Saúde de Lisboa