Radiologists from the Oxford University Hospitals (OUH) National Health Service (NHS) Foundation Trust have created a new company aimed at helping to improve medical imaging interpretation.
The group developed Report and Image Quality Control (RAIQC) as a teaching and training tool within OUH so healthcare professionals could improve their medical image interpretation skills in a secure and anonymized web-based environment.
RAIQC gives users instant feedback on how well they are interpreting medical images and allows them to anonymously benchmark themselves against their peers and experts. The software can be tailored for each user, from a medical student to an experienced professional, and can focus on the user's weaker areas.
With the creation of the company, RAIQC will be accessible beyond the NHS and can be used to support research studies that rely on imaging by, for instance, introducing quality controls on image interpretation.
Oxford University Innovation, the University of Oxford's technology transfer subsidiary, managed the commercialization of the endeavor and OUH will be a shareholder in the company. The trust will also benefit from royalties that arise from use of its image database.
![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)
