Sunday, November 28 | 1:00 p.m.-2:00 p.m. | SSMK03-3 | Room TBA
Artificial intelligence (AI) can spot changes in body composition over time on total-body dual-energy x-ray absorptiometry (DEXA) exams and predict mortality risk, according to this presentation.Researchers from the University of Hawaii hypothesized that total-body DEXA scans provide relevant body composition information beyond what's reflected in traditional risk factors. Furthermore, they theorized that tracking these body composition changes over time could improve mortality risk models.
They tested these hypotheses using deep-learning algorithms trained on data from 3,075 participants in the Health, Aging and Body Composition (Health ABC) study. Three models were trained: one using only analysis of changes in body composition from baseline and sequential total-body DEXA exams; one using only traditional mortality risk factors such as blood markers, general fitness indicators, and disability; and an algorithm that combined analysis of both DEXA information and traditional risk factors.
After testing each of the algorithms on a separate test set of cases, the researchers concluded that both of their hypotheses were confirmed.
"This approach provides a powerful way to study overall change in body composition characteristics coupled to clinical risk factors, paving a way for better intervention strategies to prolong lifespan," the authors wrote.
Attend this talk by presenter Yannik Glaser, PhD, to find out just how well the combined model performed for assessing mortality risk.
![Representative example of a 16-year-old male patient with underlying X-linked adrenoleukodystrophy. (A, B) Paired anteroposterior (AP) chest radiograph and dual-energy x-ray absorptiometry (DXA) report shows lumbar spine (L1 through L4) areal bone mineral density (BMD). The DXA report was reformatted for anonymization and improved readability. The patient had low BMD (Z score ≤ −2.0). (C) Model (chest radiography [CXR]–BMD) output shows the predicted raw BMD and Z score in comparison with the DXA reference standard, together with interpretability analyses using Shapley additive explanations (SHAP) and gradient-weighted class activation maps. The patient was classified as having low BMD, consistent with the reference standard. AM = age-matched, DEXA = dual-energy x-ray absorptiometry, RM2 = room 2, SNUH = Seoul National University Hospital, YA = young adult.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/04/ai-children-bone-density.0snnf2EJjr.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
