1. Using artificial intelligence and deep learning to optimise the selection of adult congenital heart disease patients in S-ICD screening
- Author
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Mohamed ElRefai, Mohamed Abouelasaad, Isobel Conibear, Benedict M. Wiles, Anthony J. Dunn, Stefano Coniglio, Alain B. Zemkoho, John Morgan, and Paul R. Roberts
- Subjects
Adult congenital heart disease ,S-ICD ,Deep learning ,Cardiac implantable devices ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Abstract
Introduction: The risk of complications associated with transvenous ICDs make the subcutaneous implantable cardiac defibrillator (S-ICD) a valuable alternative in patients with adult congenital heart disease (ACHD). However, higher S-ICD ineligibility and higher inappropriate shock rates-mostly caused by T wave oversensing (TWO)- are observed in this population. We report a novel application of deep learning methods to screen patients for S-ICD eligibility over a longer period than conventional screening. Methods: Adult patients with ACHD and a control group of normal subjects were fitted with a 24-h Holters 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. Results: 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
- Published
- 2024
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