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)
一位新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一篇报道的主题。根据NewsRx记者来自剑桥的新闻，Unit Ed Kingdom，研究表明，“与经静脉ICD相关的并发症风险使皮下埋植式心脏舒张器(S-ICD)成为成人先天性心脏病(ACHD)患者的一种有价值的替代方案。然而，在这一人群中，观察到更高的S-ICD不合格率和更高的不适当的S-Hock率-主要是由T波过度敏感引起我们的新闻记者从大学医院的研究中获得了一句话，“我们报告了一种新的应用深度学习方法在比常规筛选更长的时间内筛选S-ICD合格的患者。成人ACHD患者和正常对照组被装配了24小时Hol记录他们的S-ICD向量。他们的T:R比用相空间重建矩阵和基于深度学习的模型来分析，以提供对T:T：R变异采用T检验进行统计学比较。13例患者（年龄37.4±7.89岁，61.5%男性，6例ACHD患者和7例对照组),两组T:R均值和中位值差异有显著性(P<0.001),T:R标准差差异有显著性(P=0.04),T:R比值是S-ICD易感性的主要决定因素。与心脏正常的人群相比，ACHD患者明显更高，波动趋势更大。
Abstract
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.”
Key words
Cambridge/United Kingdom/Europe/Artif icial Intelligence/Cardiology/Cardiovascular Diseases and Conditions/Congenit al Diseases and Conditions/Congenital Heart Disease/Emerging Technologies/Hea rt Disease/Heart Disorders and Diseases/Machine Learning/Risk and Prevention
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