首页|University Hospital Reports Findings in Artificial Intelligence (Using artificia l intelligence and deep learning to optimise the selection of adult congenital h eart disease patients in S-ICD screening)
University Hospital Reports Findings in Artificial Intelligence (Using artificia l intelligence and deep learning to optimise the selection of adult congenital h eart disease patients in S-ICD screening)
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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 Cambridge, Unit ed Kingdom, by NewsRx correspondents, research stated, “The risk of complication s associated with transvenous ICDs make the subcutaneous implantable cardiac def ibrillator (S-ICD) a valuable alternative in patients with adult congenital hear t disease (ACHD). However, higher S-ICD ineligibility and higher inappropriate s hock rates-mostly caused by T wave oversensing (TWO)- are observed in this popul ation.” Our news journalists obtained a quote from the research from University Hospital , “We report a novel application of deep learning methods to screen patients for S-ICD eligibility over a longer period than conventional screening. Adult patie nts with ACHD and a control group of normal subjects were fitted with a 24-h Hol ters to record their S-ICD vectors. Their T:R ratio was analysed utilising phase space reconstruction matrices and a deep learning-based model to provide an in- depth description of the T: R variation plot for each vector. T: R variation was compared statistically using t-test. 13 patients (age 37.4 ± 7.89 years, 61.5 % male, 6 ACHD and 7 control subjects) were enrolled. A significant difference was observed in the mean and median T: R values between the two groups (p <0.001). There was also a significant difference in the standard deviation of T: R between both groups (p = 0.04). T:R ratio, a main determinant for S-ICD eligi bility, is significantly higher with more tendency to fluctuate in ACHD patients when compared to a population with normal hearts.”
CambridgeUnited KingdomEuropeArtif icial IntelligenceCardiologyCardiovascular Diseases and ConditionsCongenit al Diseases and ConditionsCongenital Heart DiseaseEmerging TechnologiesHea rt DiseaseHeart Disorders and DiseasesMachine LearningRisk and Prevention
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