GRANADA, SPAIN - A highlight on the opening day of the EuroSon 2019 congress was the keynote lecture from Dr. Cristina Chammas, PhD, about ultrasound evaluation of liver transplant patients.
As director of ultrasound in the Institute of Radiology at Hospital das Clínicas, University of São Paulo in Brazil, she is responsible for the imaging of about 130 liver transplant patients per year. In this video interview, she describes the type of cases she encounters, the importance of knowing the person being examined, and how the correct use of ultrasound can improve patient care.
Chammas, who is one of two vice presidents of the World Federation of Ultrasound in Medicine and Biology (WFUMB), also gives practical advice on imaging of liver transplant patients and looks ahead to WFUMB's congress in September in Melbourne, Australia.
Dr. Cristina Chammas, PhD, from São Paulo, Brazil.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
