首页|Ruhr-Universitat Bochum Reports Findings in Artificial Intelligence (Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter t ricuspid valve repair)
Ruhr-Universitat Bochum Reports Findings in Artificial Intelligence (Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter t ricuspid valve repair)
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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 originating from Bad Oeynhausen, Germany, by NewsRx correspondents, research stated, "Baseline right ventricular (RV) function derived from 3-dimensional analyses has been demonstrated to be p redictive in patients undergoing transcatheter tricuspid valve repair (TTVR). Th e complex nature of these cumbersome analyses makes patient selection based on e stablished imaging methods challenging." Our news journalists obtained a quote from the research from Ruhr-Universitat Bo chum, "Artificial intelligence (AI)-driven computed tomography (CT) segmentation of the RV might serve as a fast and predictive tool for evaluating patients pri or to TTVR. Patients suffering from severe tricuspid regurgitation underwent ful l cycle cardiac CT. AI-driven analyses were compared to conventional CT analyses . Outcome measures were correlated with survival free of rehospitalization for h eart-failure or death after TTVR as the primary endpoint. Automated AI-based ima ge CT-analysis from 100 patients (mean age 77 ± 8 years, 63% femal e) showed excellent correlation for chamber quantification compared to conventio nal, core-lab evaluated CT analysis (R 0.963-0.966; p<0.00 1). At 1 year (mean follow-up 229 ± 134 days) the primary endpoint occurred sign ificantly more frequently in patients with reduced RV ejection fraction (EF) <50 % (36.6% vs. 13.7%; HR 2.864, CI 1.21 2-6.763; p = 0.016). Furthermore, patients with dysfunctional RVs defined as end -diastolic RV volume > 210 ml and RV EF <50% demonstrated worse outcome than patients with functional RVs ( 43.7% vs. 12.2%; HR 3.753, CI 1.621-8.693; p = 0.002) . Derived RVEF and dysfunctional RV were predictors for death and hospitalizatio n after TTVR."
Bad OeynhausenGermanyEuropeArtific ial IntelligenceComputed TomographyEmerging TechnologiesImaging TechnologyMachine LearningTechnology
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