Medicolegal investigations and workforce-related issues feature prominently among AuntMinnieEurope.com's most popular articles in 2023. Our top 10 list has been published today.
The daily challenges facing radiology continue to rise sharply across Europe, which means there is an urgent need to make efficiency gains in clinical practice. Understandably, the prospect of a 7-minute MRI scan for stroke patients sounds very appealing, and this explains why our report on the topic was the most-viewed article this year.
X-ray remains an essential imaging modality, so the row over new U.K. standards for chest x-rays proved an extremely popular subject and is at number two in the top list. Another U.K. story – the murder conviction of neonatal nurse Lucy Letby – occupies the number three spot.
A report about how diagnostic services provider Unilabs sent over 170,000 imaging exams from Norwegian patients to a Romanian clinic for interpretation proved very popular. A breaking news story from Italy about an investigation into the work of two senior radiologists from Rome also caught your attention.
Below is the full top 10 list of articles on AuntMinnieEurope.com for 2023, as measured by member traffic. We very much look forward to providing you with further coverage in 2024.
Top 10 stories for 2023
- 7-minute MRI scan becomes viable option in acute stroke. Posted 28 February
- Controversy breaks out over new U.K. standards for chest x-rays. 4 July
- X-rays prove crucial in conviction of nurse who murdered 7 babies. 20 August
- Meet the 9 winners of the EuroMinnies 2023 awards. 15 February
- Unilabs embroiled in Romanian teleradiology scandal. 21 February
- MRI safety: What's the latest thinking on gadolinium retention? 9 October
- Investigation begins into work of senior Vatican radiologist. 24 July
- Radiologist's 37% error rate leads to review of scans. 6 November
- Do cancer screening exams really extend patients' lives? 28 August
- Momentum grows for MRI screening in prostate cancer. 21 August
![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&fit=crop&h=100&q=70&w=100)
![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)
