The Guardian newspaper this week ran an article on the case of a radiology manager at a U.K. National Health Service (NHS) hospital who claims she lost her job after blowing the whistle on radiologists at the facility who were allegedly moonlighting in other jobs.
Sharmila Chowdhury was a radiology manager at Ealing Hospital NHS Trust in 2008 when she raised concerns about several radiologists who she claimed were working for private hospitals despite not fulfilling their full NHS sessions, according to the article. Chowdhury claims she was sacked after reporting her concerns to hospital management.
Chowdhury won a ruling before an employment tribunal in 2010, but she remains unemployed because the hospital "restructured" her job out of existence, the Guardian reported. She received some compensation from the trust, but it went to pay for her legal fees.
Chowdury's case has become a cause célèbre amid renewed focus on whistleblowing within the NHS. A hearing was held on 18 March by Parliament's Health Committee.
![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)
