MIM Software and Stanford University are partnering to deliver telehealth to Ukraine, launching TeleHelp Ukraine support to deliver free and accessible services to Ukrainians.
On the company's end, MIM Maestro and MIMcloud, part of the MIMS Software radiation oncology suite, will provide treatment planning imaging and remote access for the Telehelp Ukraine physicians and technicians. Additionally, MIMcloud's infrastructure will allow for secure, Health Insurance Portability and Accountability Act (HIPAA), and General Data Protection Regulation (GDPR)-compliant access to patient images and information, the company said.
TeleHelp Ukraine was founded in 2022 by Stanford University medical and computer science students. It currently has over 60 practicing clinicians providing consultations in multiple specialties. The group aims to reach all Ukrainian patients with access to a computer or mobile device and stable internet access.
MIM Software plans to host ongoing MIMcloud user group training as part of its partnership with Telehelp Ukraine.
![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)
