Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort …

PJ Pickhardt, PM Graffy, R Zea, SJ Lee, J Liu… - The Lancet Digital …, 2020 - thelancet.com
Background Body CT scans are frequently done for a wide range of clinical indications, but
potentially valuable biometric information typically goes unused. We aimed to compare the …

Abdominal CT body composition thresholds using automated AI tools for predicting 10-year adverse outcomes

MH Lee, R Zea, JW Garrett, PM Graffy, RM Summers… - Radiology, 2022 - pubs.rsna.org
Background CT-based body composition measures derived from fully automated artificial
intelligence tools are promising for opportunistic screening. However, body composition …

Population-scale CT-based body composition analysis of a large outpatient population using deep learning to derive age-, sex-, and race-specific reference curves

K Magudia, CP Bridge, CP Bay, A Babic, FJ Fintelmann… - Radiology, 2021 - pubs.rsna.org
Background Although CT-based body composition (BC) metrics may inform disease risk and
outcomes, obtaining these metrics has been too resource intensive for large-scale use …

AI-based opportunistic CT screening of incidental cardiovascular disease, osteoporosis, and sarcopenia: cost-effectiveness analysis

PJ Pickhardt, L Correale, C Hassan - Abdominal Radiology, 2023 - Springer
Purpose To assess the cost-effectiveness and clinical efficacy of AI-assisted abdominal CT-
based opportunistic screening for atherosclerotic cardiovascular (CV) disease, osteoporosis …

Opportunistic screening at abdominal CT: use of automated body composition biomarkers for added cardiometabolic value

PJ Pickhardt, PM Graffy, AA Perez, MG Lubner… - Radiographics, 2021 - pubs.rsna.org
Abdominal CT is a frequently performed imaging examination for a wide variety of clinical
indications. In addition to the immediate reason for scanning, each CT examination contains …

Multimodality strategy for cardiovascular risk assessment: performance in 2 population-based cohorts

JA De Lemos, CR Ayers, BD Levine, CR DeFilippi… - Circulation, 2017 - Am Heart Assoc
Background: Current strategies for cardiovascular disease (CVD) risk assessment among
adults without known CVD are limited by suboptimal performance and a narrow focus on …

Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: A prospective study

BK Tamarappoo, A Lin, F Commandeur… - Atherosclerosis, 2021 - Elsevier
Background and aims We sought to assess the performance of a comprehensive machine
learning (ML) risk score integrating circulating biomarkers and computed tomography (CT) …

[HTML][HTML] Body composition analysis of computed tomography scans in clinical populations: the role of deep learning

MT Paris - Lifestyle genomics, 2020 - karger.com
Background: Body composition is increasingly being recognized as an important prognostic
factor for health outcomes across cancer, liver cirrhosis, and critically ill patients. Computed …

Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts

IS Forrest, BO Petrazzini, Á Duffy, JK Park… - The Lancet, 2023 - thelancet.com
Background Binary diagnosis of coronary artery disease does not preserve the complexity of
disease or quantify its severity or its associated risk with death; hence, a quantitative marker …

Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial …

JM Zambrano Chaves, AL Wentland, AD Desai… - Scientific reports, 2023 - nature.com
Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD)
events—the leading cause of global mortality—have known limitations and may be …