
The Olympic polyclinic inside Rio de Janeiro's Olympic Village -- and especially the imaging suite -- has become wildly popular with athletes and their entourages during the 2016 games, according to an August 11 story in USA Today.
The polyclinic has become especially popular for athletes from developing countries who have few options for advanced healthcare at home. Eye exams, dental x-rays, and MRI scans are being doled out by the thousands, the article states.
"They come in for everything," Marcelo Patricio, deputy chief medical officer at the clinic, told the newspaper. So far dentists have provided 450 dental x-rays and created more than 300 mouthguards. Ophthalmologists have administered 1,730 eye exams and given out 1,410 sets of prescription glasses for an Olympics that's not even halfway done. And they're performing about 60 MRI scans a day.
For athletes who complain of chronic pain or injuries, the bar is low to be cleared for an MRI scan, according to Daurio Speranzini Jr., who heads Latin American operations for GE Healthcare. The company provided handheld ultrasound and x-ray systems and two state-of-the-art MRI scanners that have been running nonstop since the games began last week.
"Those athletes from emerging countries, they really need it," Speranzini told USA Today. "I don't expect this kind of request from a U.S. guy or a U.K. guy, because they have access to this kind of technology. But in Africa, for example, few countries have this."
Russians and Ukrainians have been the most frequent users of MRI, he added.
One thing that hasn't been a problem is the Zika virus, which no one even mentions anymore, Patricio told USA Today.



![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.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)






![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.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)








