machine learning based ct ffr software (Siemens Healthineers)
86
Structured Review
Siemens Healthineers
machine learning based ct ffr software
Machine Learning Based Ct Ffr Software, supplied by Siemens Healthineers, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/machine-learning+software/ct+ffr/pmc12870152-141-6-12
Average 86 stars, based on 1 article reviews
Machine Learning Based Ct Ffr Software, supplied by Siemens Healthineers, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/machine-learning+software/ct+ffr/pmc12870152-141-6-12
Average 86 stars, based on 1 article reviews
machine learning based ct ffr software - by Bioz Stars,
2026-09
86/100 stars
Images
1) Product Images from "Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease"
Article Title: Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease
Journal: BMC Medical Imaging
doi: 10.1186/s12880-025-02146-6
Figure Legend Snippet: Flowchart of study design. DM, diabetes mellitus; CCTA, coronary computed tomography angiography; CAD, coronary artery disease; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; MACE, major adverse cardiovascular events
Techniques Used: Computed Tomography, Derivative Assay
Figure Legend Snippet: Kaplan–Meier curves for cumulative MACE rates ( A , B , C ) and cumulative MACCE rates ( D , E , F ) for stratified groups based on HRP, CT-FFR, and pericoronary FAI. MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; HU, Hounsfield units
Techniques Used: Derivative Assay
Figure Legend Snippet: ROC curves of all models in predicting MACE ( A ) and MACCE ( B ). Model 1: HRP; Model 2: CT-FFR; Model 3: pericoronary FAI; Model 4: HRP + CT-FFR; Model 5: Model 4 + pericoronary FAI. ROC, receiver operating characteristic; MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; AUC, area under the curve; CI, confidence interval
Techniques Used: Derivative Assay
Figure Legend Snippet: A representative case of DM patients with non-obstructive CAD. CCTA showed CAD-RADS 2, with 25–49% stenosis in LAD as well as 1–24% stenosis in RCA, and there was a HRP characterized by low attenuation plaque and spotty calcification in LAD; CT-FFR was 0.88; pericoronary FAI was − 60.97 HU. This patient underwent acute non-ST-segment elevation myocardial infarction 40 months after CCTA. DM, diabetes mellitus; CAD, coronary artery disease; CCTA, coronary computed tomography angiography; CAD-RADS, Coronary Artery Disease-Reporting and Data System; LAD, left anterior descending; LCX, left circumflex; RCA, right coronary artery; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; HU, Hounsfield units
Techniques Used: Computed Tomography, Derivative Assay
Related Articles
Software:Article Title: Prognostic value of non-alcoholic fatty liver disease, pericoronary fat attenuation index and computed tomography-derived fractional flow reserve in diabetic patients with suspected coronary artery disease. Article Snippet: Background: This study aimed to investigate the predictive value of non-alcoholic fatty liver disease (NAFLD), pericoronary fat attenuation index (FAI), and computed tomography-derived fractional flow reserve (CT-FFR) for major adverse cardiovascular events (MACE) in diabetic patients with suspected coronary artery disease.. Methods: This study retrospectively included 466 diabetic patients who underwent coronary computed tomography angiography (CCTA) and non-contrast chest CT from January 2017 to December 2018.. The clinical data and imaging parameters of patients were collected. Article Title: CT Myocardial Perfusion and CT-FFR versus Invasive FFR for Hemodynamic Relevance of Coronary Artery Disease. Article Snippet: .. CT-FFR.—On-site machine learning research software was used for Article Title: Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease. Article Snippet: Objectives To compare the diagnostic performance of machine learning (ML)–based computed tomography– derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis.. Methods Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2 weeks.. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Article Title: Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease Article Snippet: .. CT-FFR analysis was performed using a Article Title: Quantitative pulmonary perfusion in acute pulmonary embolism and chronic thromboembolic pulmonary hypertension Article Snippet: .. After de‐identification, the 100 and 140 kVp image datasets were exported offline and processed with a stand‐alone, machine‐learning based software for quantitative other:Article Title: Low-attenuation coronary plaque burden and troponin release in chronic coronary syndrome: A mediation analysis. Article Snippet: The generation of Article Title: Fully Automated Artery-Specific Calcium Scoring Based on Machine Learning in Low-Dose Computed Tomography Screening. Article Snippet: Isolation:Article Title: Quantitative pulmonary perfusion in acute pulmonary embolism and chronic thromboembolic pulmonary hypertension Article Snippet: .. After de‐identification, the 100 and 140 kVp image datasets were exported offline and processed with a stand‐alone, machine‐learning based software for quantitative |