The supply of radiographers in Lithuania during the next two decades will be insufficient to meet the country's needs, according to a study published online on 16 April in the European Journal of Radiology.
Researchers from the Hospital of Lithuanian University of Health Sciences Kaunas Clinics found that the number of students choosing to become radiographers must increase by as much as 30% to meet the forecasted demand.
The aim of this study was to analyze the supply and demand for radiographers in the labor market with respect to their number, structure, and services, and to provide a prognosis for the years 2012 to 2030. The anticipated demand for radiographers was based on the population's need for outpatient radiology services, such as CT and MRI scans.
Based on the projections, the shortage will increase from 0.13 full-time equivalents per 10,000 people to 0.37 full-time equivalents per 10,000 in 2030.
![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)
