首页|University of Catania Reports Findings in Artificial Intelligence(Non-invasive physiological assessment of intermediate coronarystenoses from plain angiograph y through artificial intelligence: theSTARFLOW system)
University of Catania Reports Findings in Artificial Intelligence(Non-invasive physiological assessment of intermediate coronarystenoses from plain angiograph y through artificial intelligence: theSTARFLOW system)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Artificial Intelligenc e is the subject of a report. According tonews reporting originating in Catania , Italy, by NewsRx journalists, research stated, “Despite evidencesupporting us e of fractional flow reserve (FFR) and instantaneous waves-free ratio (iFR) to i mproveoutcome of patients undergoing coronary angiography (CA) and percutaneous coronary intervention, suchtechniques are still underused in clinical practice due to economic and logistic issues. We aimed to developan artificial intellig ence (AI)-based application to compute FFR and iFR from plain CA.”The news reporters obtained a quote from the research from the University of Cat ania, “Consecutivepatients performing FFR or iFR or both were enrolled. A speci fic multi-task deep network exploiting 2projections of the coronary of interest from standard CA was appraised. Accuracy of prediction of FFR/iFRof the AI mod el was the primary endpoint, along with sensitivity and specificity. Prediction was testedboth for continuous values and for dichotomous classification (positi ve/negative) for FFR or iFR. Subgroupanalyses were performed for FFR and iFR.A total of 389 patients from 5 centers were enrolled. Mean agewas 67.9 ? 9.6 and 39.2% of patients were admitted for acute coronary syndrome. Overa ll, the accuracywas 87.3% (81.2-93.4%), with a sensi tivity of 82.4% (71.9-96.4%) and a specificity of 92. 2% (90.4-93.9%). For FFR, accuracy was 84.8% (77.8-91.8%), with a sensitivity of 81.9% (69.4-94.4% ) and aspecificity of 87.7% (85.5-89.9%), while for iFR accuracy was 90.2% (86.0-94.6%), with a sensitivi ty of87.2% (76.6-97.8%) and a specificity of 93.2% (91.7-94.7%, all confidence intervals 95%).”
CataniaItalyEuropeAngiographyArt ificial IntelligenceCardiologyCardiovascular Diagnostic TechniquesEmerging TechnologiesHealth and MedicineMachineLearning
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