首页|Turku University Hospital Reports Findings in Artificial Intelligence (Prognosti c value of a novel artificial intelligence-based coronary CTA-derived ischemia a lgorithm among patients with normal or abnormal myocardial perfusion)
Turku University Hospital Reports Findings in Artificial Intelligence (Prognosti c value of a novel artificial intelligence-based coronary CTA-derived ischemia a lgorithm among patients with normal or abnormal myocardial perfusion)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the su bject of a report. According to news reporting originating from Turku, Finland, by NewsRx correspondents, research stated, “Among patients with obstructive coro nary artery disease (CAD) on coronary computed tomography angiography (CTA), dow nstream positron emission tomography (PET) perfusion imaging can be performed to assess the presence of myocardial ischemia. A novel artificial-intelligence-gui ded quantitative computed tomography ischemia algorithm (AI-QCT) aims to predict ischemia directly from coronary CTA images.” Our news editors obtained a quote from the research from Turku University Hospit al, “We aimed to study the prognostic value of AI-QCT among patients with obstru ctive CAD on coronary CTA and normal or abnormal downstream PET perfusion. AI-QC T was calculated by blinded analysts among patients from the retrospective coron ary CTA cohort at Turku University Hospital, Finland, with obstructive CAD on in itial visual reading (diameter stenosis 50%) being referred for dow nstream O-HO-PET adenosine stress perfusion imaging. All coronary arteries with their side branches were assessed by AI-QCT. Absolute stress myocardial blood fl ow 2.3 ml/g/min in 2 adjacent segments was considered abnormal. The primary endp oint was death, myocardial infarction, or unstable angina pectoris. The median f ollow-up was 6.2 [IQR 4.4-8.3] years. 662 of 768 (86%) patients had conclusive AI-QCT result. In patients wit h normal O-HO-PET perfusion, an abnormal AI-QCT result (n = 147/331) vs. normal AI-QCT result (n = 184/331) was associated with a significantly higher crude and adjusted rates of the primary endpoint (adjusted HR 2.47, 95% CI 1.17-5.21, p = 0.018). This did not pertain to patients with abnormal O-HO-PET p erfusion (abnormal AI-QCT result (n = 269/331) vs. normal AI-QCT result (n = 62/ 331); adjusted HR 1.09, 95 % CI 0.58-2.02, p = 0.794) (p-interactio n = 0.039).”
TurkuFinlandEuropeAlgorithmsArti ficial IntelligenceCardiologyCoronary Artery DiseaseEmerging TechnologiesHealth and MedicineHeart DiseaseHeart Disorders and DiseasesIschemiaMac hine LearningPerfusionVascular Diseases and Conditions
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