Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems.

Objective: We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems.

Methods: A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death.

Sex differences in computed tomography angiography-derived coronary plaque burden in relation to invasive fractional flow reserve.

Background: Distinct sex-related differences exist in coronary artery plaque burden and distribution. We aimed to explore sex differences in quantitative plaque burden by coronary CT angiography (CCTA) in relation to ischemia by invasive fractional flow reserve (FFR).

Methods: This post-hoc analysis of the PACIFIC trial included 581 vessels in 203 patients (mean age 58.

Relationship between impaired myocardial blood flow by positron emission tomography and low-attenuation plaque burden and pericoronary adipose tissue attenuation from coronary computed tomography: From the prospective PACIFIC trial.

Background: Positron emission tomography (PET) is the clinical gold standard for quantifying myocardial blood flow (MBF). Pericoronary adipose tissue (PCAT) attenuation may detect vascular inflammation indirectly. We examined the relationship between MBF by PET and plaque burden and PCAT on coronary CT angiography (CCTA).

Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow.

Background: A pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography (CCTA) for cardiovascular risk stratification. We sought to assess the performance of a ML score integrating CCTA-based quantitative plaque features for predicting vessel-specific ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by positron emission tomography (PET).

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks.

Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion). We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks.

Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography Angiography.

Objectives: The aim of this study was to precisely phenotype culprit and nonculprit lesions in myocardial infarction (MI) and lesions in stable coronary artery disease (CAD) using coronary computed tomography angiography (CTA)-based radiomic analysis.

Background: It remains debated whether any single coronary atherosclerotic plaque within the vulnerable patient exhibits unique morphology conferring an increased risk of clinical events.

Methods: A total of 60 patients with acute MI prospectively underwent coronary CTA before invasive angiography and were matched to 60 patients with stable CAD.

Hepatosteatosis and Atherosclerotic Plaque at Coronary CT Angiography.

Purpose: To assess the association between nonalcoholic fatty liver disease (NAFLD) and quantitative atherosclerotic plaque at CT.

Materials And Methods: In this post hoc analysis of the prospective Scottish Computed Tomography of the HEART trial (November 2010 to September 2014), hepatosteatosis and coronary artery calcium score were measured at noncontrast CT. Presence of stenoses, visually assessed high-risk plaque, and quantitative plaque burden were assessed at coronary CT angiography.

Pericoronary Adipose Tissue Attenuation, Low-Attenuation Plaque Burden, and 5-Year Risk of Myocardial Infarction.

Background: Pericoronary adipose tissue (PCAT) attenuation and low-attenuation noncalcified plaque (LAP) burden can both predict outcomes.

Objectives: This study sought to assess the relative and additive values of PCAT attenuation and LAP to predict future risk of myocardial infarction.

Methods: In a post hoc analysis of the multicenter SCOT-HEART (Scottish Computed Tomography of the Heart) trial, the authors investigated the relationships between the future risk of fatal or nonfatal myocardial infarction and PCAT attenuation measured from coronary computed tomography angiography (CTA) using multivariable Cox regression models including plaque burden, obstructive coronary disease, and cardiac risk score (incorporating age, sex, diabetes, smoking, hypertension, hyperlipidemia, and family history).

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study.

Background: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity.

Methods: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets.

Association of coronary artery calcium score with qualitatively and quantitatively assessed adverse plaque on coronary CT angiography in the SCOT-HEART trial.

Aims: Coronary artery calcification is a marker of cardiovascular risk, but its association with qualitatively and quantitatively assessed plaque subtypes is unknown.

Methods And Results: In this post-hoc analysis, computed tomography (CT) images and 5-year clinical outcomes were assessed in SCOT-HEART trial participants. Agatston coronary artery calcium score (CACS) was measured on non-contrast CT and was stratified as zero (0 Agatston units, AU), minimal (1-9 AU), low (10-99 AU), moderate (100-399 AU), high (400-999 AU), and very high (≥1000 AU).

Sex-Specific Computed Tomography Coronary Plaque Characterization and Risk of Myocardial Infarction.

Objectives: This study was designed to investigate whether coronary computed tomography angiography assessments of coronary plaque might explain differences in the prognosis of men and women presenting with chest pain.

Background: Important sex differences exist in coronary artery disease. Women presenting with chest pain have different risk factors, symptoms, prevalence of coronary artery disease and prognosis compared to men.

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks.

Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in Coronavirus disease 2019 (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (LSTM) networks. Utilizing the expert annotations, model training was performed using 5-fold cross-validation to segment ground-glass opacity and high opacity (including consolidation and pleural effusion).

Quantitative Burden of COVID-19 Pneumonia on Chest CT Predicts Adverse Outcomes: A Post-Hoc Analysis of a Prospective International Registry.

Purpose: To examine the independent and incremental value of CT-derived quantitative burden and attenuation of COVID-19 pneumonia for the prediction of clinical deterioration or death.

Methods: This was a retrospective analysis of a prospective international registry of consecutive patients with laboratory-confirmed COVID-19 and chest CT imaging, admitted to four centers between January 10 and May 6, 2020. Total burden (expressed as a percentage) and mean attenuation of ground glass opacities (GGO) and consolidation were quantified from CT using semi-automated research software.

Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study.

Background: We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term risk of major adverse cardiovascular events (MACE) in asymptomatic individuals.

Methods: This was a post-hoc analysis of the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) study of participants who underwent baseline coronary artery calcium (CAC) scoring CT and 14-year follow-up for MACE (myocardial infarction, late revascularization, or cardiac death). EAT volume (cm) and attenuation (Hounsfield units [HU]) were quantified from CT using fully automated deep learning software (< 30 s per case).

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

Background And Aims: We sought to assess the performance of a comprehensive machine learning (ML) risk score integrating circulating biomarkers and computed tomography (CT) measures for the long-term prediction of hard cardiac events in asymptomatic subjects.

Methods: We studied 1069 subjects (age 58.2 ± 8.

Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score.

Objectives: The machine learning ischemia risk score (ML-IRS) is a machine learning-based algorithm designed to identify hemodynamically significant coronary disease using quantitative coronary computed tomography angiography (CCTA). The purpose of this study was to examine whether the ML-IRS can predict revascularization in patients referred for invasive coronary angiography (ICA) after CCTA.

Methods: This study was a post hoc analysis of a prospective dual-center registry of sequential patients undergoing CCTA followed by ICA within 3 months, referred from inpatient, outpatient, and emergency department settings (n = 352, age 63 ± 10 years, 68% male).

Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control Study.

Objectives: This study sought to determine whether coronary computed tomography angiography (CCTA)-based radiomic analysis of pericoronary adipose tissue (PCAT) could distinguish patients with acute myocardial infarction (MI) from patients with stable or no coronary artery disease (CAD).

Background: Imaging of PCAT with CCTA enables detection of coronary inflammation. Radiomics involves extracting quantitative features from medical images to create big data and identify novel imaging biomarkers.

Repeatability of quantitative pericoronary adipose tissue attenuation and coronary plaque burden from coronary CT angiography.

Background: High pericoronary adipose tissue (PCAT) attenuation and non-calcified plaque burden (NCP) measured from coronary CT angiography (CTA) have been implicated in future cardiac events. We aimed to evaluate the interobserver and intraobserver repeatability of PCAT attenuation and NCP burden measurement from CTA, in a sub-study of the prospective SCOT-HEART trial.

Methods: Fifty consecutive CTAs from participants of the CT arm of the prospective SCOT-HEART trial were included.

Low-Attenuation Noncalcified Plaque on Coronary Computed Tomography Angiography Predicts Myocardial Infarction: Results From the Multicenter SCOT-HEART Trial (Scottish Computed Tomography of the HEART).

Background: The future risk of myocardial infarction is commonly assessed using cardiovascular risk scores, coronary artery calcium score, or coronary artery stenosis severity. We assessed whether noncalcified low-attenuation plaque burden on coronary CT angiography (CCTA) might be a better predictor of the future risk of myocardial infarction.

Methods: In a post hoc analysis of a multicenter randomized controlled trial of CCTA in patients with stable chest pain, we investigated the association between the future risk of fatal or nonfatal myocardial infarction and low-attenuation plaque burden (% plaque to vessel volume), cardiovascular risk score, coronary artery calcium score or obstructive coronary artery stenoses.

Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.

Background: Epicardial adipose tissue (EAT) volume (cm) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenuation measurements quantified from noncontrast cardiac computed tomography.

Methods: Our study included 2068 asymptomatic subjects (56±9 years, 59% male) from the EISNER trial (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) with long-term follow-up after coronary artery calcium measurement.

Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study.

Aims: Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardial infarction (MI) and cardiac death in asymptomatic subjects.

Methods And Results: Our study included 1912 asymptomatic subjects [1117 (58.4%) male, age: 55.