Vista.ai plans to highlight study results that show its technology reduces cardiac MRI scanning time at the upcoming Society for Cardiovascular Magnetic Resonance (SCMR) meeting in San Diego.
Researchers led by Dr. Raymond Kwong from Brigham and Women's Hospital in Boston will present the findings, according to the firm. The study evaluated the use of Vista's One Click MRI for identifying cardiomyopathy and structural heart disease; the researchers compared traditional cardiac MRI exams against both partial and full AI-assisted scans taken between April and September 2022.
Among the team's findings, full AI-assisted scans were 31% shorter than non-AI scans, while 90% of full AI-assisted scans were completed within 45 minutes. In contrast, about 25% of unassisted scans were completed within that time frame.
Also, full-AI assisted scan times were threefold more consistent than unassisted scan times. Both had minimum times of 26 to 27 minutes, but maximum times were 64 minutes versus 161 minutes, respectively, Vista.ai said.
![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)
