Erik L. Ridley, Kate Madden Yee, Abraham Kim, Wayne Forrest, and Brian Casey[email protected]2018Top 5 trends from RSNA 2018 in ChicagoStop us if you've heard this before, but there was a dominant theme at this year's RSNA 2018 meeting in Chicago. And that theme, believe it or not, was artificial intelligence (AI). As at RSNA 2017, sessions on AI and the related topics of machine learning and deep learning dominated the halls of McCormick Place. However, the debate around AI has begun to shift in subtle ways.December 12, 20182017Top 5 trends from RSNA 2017 in ChicagoSometimes it's hard to come up with a theme for a given RSNA conference, but in 2017 it's easy -- talk of artificial intelligence (AI) dominated the discussion at RSNA 2017. Attendees stuffed lectures on AI to the point where people were turned away from crowded rooms, while vendors took advantage of heightened interest to make claims about AI-powered products that sometimes strained credulity.December 12, 2017Page 1 of 1Top StoriesUltrasoundPOCUS assistance leads to no improvements in NICU lumbar puncturesUltrasound assistance does not lead to improvements in first-attempt non-traumatic lumbar puncture success for NICU patients.AIImaging fellow tries vibe coding to solve radiology challengesISMRM 2026ISMRM annual meeting in Cape Town to highlight South African 'Ubuntu'InterventionalRadiation segmentectomy can be optimized before liver transplantSponsor ContentRadiology is Under Pressure
![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)
