MRI reconstruction technology firm AIRS Medical is partnering with medical imaging firm Incepto to integrate its SwiftMR tool into Incepto's Folio platform.
The companies said that the integration of SwiftMR into Folio will expand access to AIRS’ deep-learning technology across the European Union, enabling hospitals and clinics to simplify workflows and expand patient access to high-quality care.
Founded in 2018 in France, Incepto has a presence in 350 clinics and the capacity to process more than 400,000 radiological exams each month.
Financial and other terms of the deal were not disclosed.
SwiftMR can be integrated into any existing MRI machine. It uses deep learning to speed up MRI scan times and thus improve image quality. Seoul, South Korea-based AIRS Medical added that, along with streamlining workflows and enhancing patient care, SwiftMR can help clinicians come to more confident diagnoses, eliminate backlogs, and reduce the need for costly upgrades.
![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)
