首页|Fudan University Reports Findings in Heart Disease (Comparison of machine learni ng-based CT fractional flow reserve with cardiac MR perfusion mapping for ischem ia diagnosis in stable coronary artery disease)
Fudan University Reports Findings in Heart Disease (Comparison of machine learni ng-based CT fractional flow reserve with cardiac MR perfusion mapping for ischem ia diagnosis in stable coronary artery disease)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Heart Disorders and Di seases-Heart Disease is the subject of a report. According to news reporting f rom Shanghai, People's Republic of China, by NewsRx journalists, research stated, "To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resona nce (MR) perfusion mapping for functional assessment of coronary stenosis. Betwe en October 2020 and March 2022, consecutive participants with stable coronary ar tery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardi ac MR, and invasive fractional flow reserve (FFR) within 2 weeks." The news correspondents obtained a quote from the research from Fudan University, "Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenos is was defined as FFR 0.8 or > 90% stenosi s on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference. The study protocol was completed in 110 participants (mean age, 62 years ± 8; 73 men), and hemodyn amically significant stenosis was detected in 36 (33%). Among the q uantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant s tenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0. 89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperf ormed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with im proved specificity (95% vs 82%, p = 0.01) and accurac y (95% vs 81%, p<0.01). ML-base d CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantit ative perfusion mapping had a favorable performance in per-patient analysis."
ShanghaiPeople's Republic of ChinaAs iaAngiologyArterial Occlusive DiseasesArteriosclerosisCardiologyCardio vascular Diseases and ConditionsCoronary ArteryCoronary Artery DiseaseCybo rgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineHear t DiseaseHeart Disorders and DiseasesIschemiaMachine LearningMyocardial IschemiaPerfusionStenosisVascular Diseases and Conditions
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