首页|University of Tubingen Reports Findings in Artificial Intelligence (Artificial i ntelligence-enhanced detection of subclinical coronary artery disease in athlete s: diagnostic performance and limitations)
University of Tubingen Reports Findings in Artificial Intelligence (Artificial i ntelligence-enhanced detection of subclinical coronary artery disease in athlete s: diagnostic performance and limitations)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Tubingen, Germany , by NewsRx journalists, research stated, “This study evaluates the diagnostic p erformance of artificial intelligence (AI)-based coronary computed tomography an giography (CCTA) for detecting coronary artery disease (CAD) and assessing fract ional flow reserve (FFR) in asymptomatic male marathon runners. We prospectively recruited 100 asymptomatic male marathon runners over the age of 45 for CAD scr eening.” Financial support for this research came from Universitatsklinikum Tubingen. The news correspondents obtained a quote from the research from the University o f Tubingen, “CCTA was analyzed using AI models (CorEx and Spimed-AI) on a local server. The models focused on detecting significant CAD ( 50% diam eter stenosis, CAD-RADS 3, 4, or 5) and distinguishing hemodynamically significa nt stenosis (FFR 0.8) from non-significant stenosis (FFR > 0.8). Statistical analysis included sensitivity, specificity, positive predicti ve value (PPV), negative predictive value (NPV), and accuracy. The AI model demo nstrated high sensitivity, with 91.2% for any CAD and 100% for significant CAD, and high NPV, with 92.7% for any CAD and 100% for significant CAD. The diagnostic accuracy was 73.4% for any CAD and 90.4% for significant CAD. However, the PPV was lower, partic ularly for significant CAD (25.0%), indicating a higher incidence o f false positives. AI-enhanced CCTA is a valuable non-invasive tool for detectin g CAD in asymptomatic, low-risk populations. The AI model exhibited high sensiti vity and NPV, particularly for identifying significant stenosis, reinforcing its potential role in screening. However, limitations such as a lower PPV and overe stimation of disease indicate that further refinement of AI algorithms is needed to improve specificity.”
TubingenGermanyEuropeAngiologyAr terial Occlusive DiseasesArteriosclerosisArtificial IntelligenceCardiologyCardiovascular Diseases and ConditionsCoronary ArteryCoronary Artery Disea seDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineHeart DiseaseHeart Disorders and DiseasesMachine LearningMyocardial IschemiaS tenosis
NETL
NSTL
