This study compared AI-based lung cancer likelihood stratification with retrospectively reconstructed Lung-RADS version 1.0 in two baseline low-dose CT screening cohorts: the U.S. National Lung Screening Trial (US-NLST) and the Pamplona International Early Lung Cancer Action Program (P-IELCAP). The primary outcome was histologically confirmed lung cancer within one year. Study population and approach The analysis included 2,342 US-NLST examinations with 40 one-year cancers an

This review article presents AI-CVD as a multidimensional framework for extracting cardiovascular and cardiometabolic risk information from coronary artery calcium (CAC) scans and routine chest CT. Rather than relying on a single measurement, the platform brings together imaging biomarkers across the heart, vessels, lungs, liver, muscle, adipose tissue, and bone. What the framework assesses The manuscript describes AI-enabled assessment of coronary artery calcium and plaque f

A concise review of the AI-CVD study comparing automated cardiac chamber measurements from routine, non-gated chest CT with echocardiography in the Multi-Ethnic Study of Atherosclerosis (MESA). Study design The analysis included 1,675 MESA participants who underwent both non-contrast, non-gated chest CT and echocardiography at Exam 6 (2016–2018). An AI-enabled system (AutoChamber™) measured cardiac chambers on CT. The study evaluated how these measurements identified prevalen

A scientific review of an AI-CVD study showing how automated cardiac chamber volumetry on non-gated chest CT scans may identify future risk of heart failure and atrial fibrillation.