Dear AuntMinnie Member,
Medical imaging was key to disclosing intriguing findings about a 3,000-year-old patient recently seen in Poland -- including the patient's sex.
Researchers at the National Museum in Warsaw thought that a mummy that had been in the museum's collection for over 100 years was a male priest who had been entombed at a vast necropolis in Thebes, Egypt. But after scanning the mummy with CT and x-ray, they soon discovered it was of a woman who was pregnant when she died.
In other x-ray news, be sure to check out our article on the new Medical Imaging and Data Resource Center database being developed to house medical images of COVID-19 patients. The story is based on a presentation by Maryellen Giger, PhD, at our Spring 2021 Virtual Conference: Advances in AI, this past week.
Get these stories and more in our Digital X-Ray Community.
AI predicts COVID-19 mortality
Speaking of artificial intelligence (AI), researchers from Massachusetts General Hospital have developed an AI algorithm that they believe can provide highly accurate predictions of the clinical outcomes -- including mortality -- in patients with COVID-19.
AI has many opportunities in screening mammography, according to another talk at our virtual conference, this one by Dr. Emily Conant. She reviewed the current state of affairs in AI for breast screening, concluding that AI has an exciting future in mammography and should be embraced.
Finally, thanks to everyone who attended Advances in AI on May 5-6. The meeting featured two days of high-quality talks on AI in radiology, and we were pleased to receive such a positive response. Don't worry if you weren't able to attend in person -- we'll be posting recordings of the talks to our Conferences page in the next few days.
![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.auntminnie.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)
