An Artificial Neural Network for the Electrocardiographic Diagnosis of Left Ventricular Hypertrophy

2000 ◽  
Vol 28 (3-4) ◽  
pp. 435-438
Author(s):  
Christie B. Hopkins ◽  
Jawal Suleman ◽  
Carl Cook
EP Europace ◽  
2019 ◽  
Vol 22 (3) ◽  
pp. 412-419 ◽  
Author(s):  
Joon-Myoung Kwon ◽  
Ki-Hyun Jeon ◽  
Hyue Mee Kim ◽  
Min Jeong Kim ◽  
Sung Min Lim ◽  
...  

Abstract Aims  Although left ventricular hypertrophy (LVH) has a high incidence and clinical importance, the conventional diagnosis criteria for detecting LVH using electrocardiography (ECG) has not been satisfied. We aimed to develop an artificial intelligence (AI) algorithm for detecting LVH. Methods and results This retrospective cohort study involved the review of 21 286 patients who were admitted to two hospitals between October 2016 and July 2018 and underwent 12-lead ECG and echocardiography within 4 weeks. The patients in one hospital were divided into a derivation and internal validation dataset, while the patients in the other hospital were included in only an external validation dataset. An AI algorithm based on an ensemble neural network (ENN) combining convolutional and deep neural network was developed using the derivation dataset. And we visualized the ECG area that the AI algorithm used to make the decision. The area under the receiver operating characteristic curve of the AI algorithm based on ENN was 0.880 (95% confidence interval 0.877–0.883) and 0.868 (0.865–0.871) during the internal and external validations. These results significantly outperformed the cardiologist’s clinical assessment with Romhilt-Estes point system and Cornell voltage criteria, Sokolov-Lyon criteria, and interpretation of ECG machine. At the same specificity, the AI algorithm based on ENN achieved 159.9%, 177.7%, and 143.8% higher sensitivities than those of the cardiologist’s assessment, Sokolov-Lyon criteria, and interpretation of ECG machine. Conclusion  An AI algorithm based on ENN was highly able to detect LVH and outperformed cardiologists, conventional methods, and other machine learning techniques.


2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  

2014 ◽  
Vol 19 (2) ◽  
pp. 11-15
Author(s):  
Steven L. Demeter

Abstract The fourth, fifth, and sixth editions of the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides) use left ventricular hypertrophy (LVH) as a variable to determine impairment caused by hypertensive disease. The issue of LVH, as assessed echocardiographically, is a prime example of medical science being at odds with legal jurisprudence. Some legislatures have allowed any cause of LVH in a hypertensive individual to be an allowed manifestation of hypertensive changes. This situation has arisen because a physician can never say that no component of LVH was not caused by the hypertension, even in an individual with a cardiomyopathy or valvular disorder. This article recommends that evaluators consider three points: if the cause of the LVH is hypertension, is the examinee at maximum medical improvement; is the LVH caused by hypertension or another factor; and, if apportionment is allowed, then a careful analysis of the risk factors for other disorders associated with LVH is necessary. The left ventricular mass index should be present in the echocardiogram report and can guide the interpretation of the alleged LVH; if not present, it should be requested because it facilitates a more accurate analysis. Further, if the cause of the LVH is more likely independent of the hypertension, then careful reasoning and an explanation should be included in the impairment report. If hypertension is only a partial cause, a reasoned analysis and clear explanation of the apportionment are required.


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