The U.K. Royal College of Radiologists (RCR) is lauding a report on interventional radiology (IR) safety released 3 June by the Healthcare Safety Investigation Branch (HSIB).
The report addresses the risks of misidentifying outpatients scheduled for interventional procedures and was prompted in part by an investigation into a case in which a woman presenting at a gynecology clinic for a fertility assessment was mistaken for another patient and underwent a colposcopy procedure.
The report recommends that National Health Service England review its communication among providers, staff workloads, and IT capability for interventional radiology procedures.
"A significant majority of IR procedures are delivered in a day case or outpatient setting, making the HSIB report pertinent to IR practice," said Dr. Jai Patel, chair of the RCR's Interventional Radiology Committee, in a statement released by the college. "Correct patient identification is also relevant to diagnostic radiology, where large volumes of imaging examinations -- some utilizing ionizing radiation -- are performed on a daily basis in an outpatient setting."
![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)
