Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).

Background: The National Lung Screening Trial (NLST) demonstrated that annual screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. Nonetheless, the leading cause of mortality in the study was from cardiovascular diseases.

Purpose: To determine whether the used machine learning automatic algorithms assessing coronary calcium score (CCS), level of liver steatosis and emphysema percentage in the lungs are good predictors of cardiovascular disease (CVD) mortality and incidence when applied on low dose CT scans.

Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization.

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA) and risk predictors like the Fracture Risk Assessment Tool (FRAX), are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex.

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade.

Purpose: Osteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, although recommended in clinical guidelines, remains markedly underutilized. In contrast to DEXA, CT utilization is high and presents a valuable data source for opportunistic osteoporosis screening.