首页|University of Notre Dame Reports Findings in Artificial Intelligence(An Artific ial Intelligence Algorithm for Detection of Severe AorticStenosis: A Clinical C ohort Study)
University of Notre Dame Reports Findings in Artificial Intelligence(An Artific ial Intelligence Algorithm for Detection of Severe AorticStenosis: A Clinical C ohort Study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – New research on Artificial Intelligence is the su bject of a report. According to news reportingoriginating in Fremantle, Austral ia, by NewsRx journalists, research stated, “Identifying individuals withsevere aortic stenosis (AS) at high risk of mortality remains challenging using curren t clinical imagingmethods. The purpose of this study was to evaluate an artific ial intelligence decision support algorithm(AI-DSA) to augment the detection of severe AS within a well-resourced health care setting.”The news reporters obtained a quote from the research from the University of Not re Dame, “Agnostic toclinical information, an AI-DSA trained to identify echoca rdiographic phenotype associated with an aorticvalve area (AVA) <1 cm using minimal input data (excluding left ventricular outflow tract measures ) wasapplied to routine transthoracic echocardiograms (TTE) reports from 31,141 U.S. Medicare beneficiariesat an academic medical center (2003-2017). Performa nce of AI-DSA to detect the phenotype associatedwith an AVA <1 cm was excellent (sensitivity 82.2%, specificity 98.1% , negative predictive value 9.2%,c-statistic = 0.986). In addition to identifying clinical severe AS cases, AI-DSA identified an additional1,034 (3.3%) individuals with guideline-defined moderate AS but with a si milar clinical and TTE phenotypeto those with severe AS with low rates of aorti c valve replacement (6.6%). Five-year mortality was 75.9% in those with known severe AS, 73.5% in those with a similar pheno type to severe AS, and 44.6% inthose without severe AS. The AI-DS A continued to perform well to identify severe AS among those with adepressed l eft ventricular ejection fraction. Overall rates of aortic valve replacement rem ained low, evenin those with an AVA <1 cm (21.9% ). Without relying on left ventricular outflow tract measurements,an AI-DSA use d echocardiographic reports to reliably identify the phenotype of severe AS.”
FremantleAustraliaAustralia and New ZealandAlgorithmsAngiologyAortic StenosisAortic Valve StenosisArtifici al IntelligenceCardiologyCardiovascular Diseasesand ConditionsClinical Re searchClinical Trials and StudiesEmerging TechnologiesGeneticsHealthand MedicineMachine LearningRisk and PreventionStenosis
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