Lea Philine Hoffmann
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Lea Philine Hoffmann has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
- Antonius Büscher
Institute of Medical Informatics, University of Münster , Albert-Schweitzer-Campus 1/Building A11,Münster 48149
1 shared publication - Benjamin Rath
Department of Cardiology II (Electrophysiology), University Hospital Münster, Münster, Germany
1 shared publication - Eric Schulze-Bahr
Department of Cardiovascular Medicine, Institute for Genetics of Heart Diseases (IfGH), University Hospital Münster, Germany
1 shared publication - Fatih Güner
Department of Cardiology II: Electrophysiology, University Hospital Münster, Albert-Schweitzer Campus 1, 48149 Münster, Germany
1 shared publication - Felix Konrad Wegner
Department for Cardiology II: Electrophysiology, University Hospital Münster, 48149 Münster, Germany
1 shared publication - Florian Doldi
Department for Cardiology II: Electrophysiology, University Hospital Münster, Münster, Germany
1 shared publication
Latest Publications
- Open Access
- 13 July 2022
- Published by MDPI AG in Journal of Personalized Medicine
- Vol. 12 (7) , 1135
Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models
Introduction: The long-QT syndrome (LQTS) is the most common ion channelopathy, typically presenting with a prolonged QT interval and clinical symptoms such as syncope or sudden cardiac death. Patients may present with a concealed phenotype making the diagnosis challenging. Correctly diagnosing at-risk patients is pivotal to starting early preventive treatment. Objective: Identification of congenital and often concealed LQTS by utilizing novel deep learning network architectures, which are specifically designed for multichannel time series and therefore particularly suitable for ECG data. Design and Results: A retrospective artificial intelligence (AI)-based analysis was performed using a 12-lead ECG of genetically confirmed LQTS (n = 124), including 41 patients with a concealed LQTS (33%), and validated against a control cohort (n = 161 of patients) without known LQTS or without QT-prolonging drug treatment but any other cardiovascular disease. The performance of a fully convolutional network (FCN) used in prior studies was compared with a different, novel convolutional neural network model (XceptionTime). We found that the XceptionTime model was able to achieve a higher balanced accuracy score (91.8%) than the associated FCN metric (83.6%), indicating improved prediction possibilities of novel AI architectures. The predictive accuracy prevailed independently of age and QTc parameters. Conclusions: In this study, the XceptionTime model outperformed the FCN model for LQTS patients with even better results than in prior studies. Even when a patient cohort with cardiovascular comorbidities is used. AI-based ECG analysis is a promising step for correct LQTS patient identification, especially if common diagnostic measures might be misleading.
- 13 July 2022
- Published by MDPI AG in Journal of Personalized Medicine
- Vol. 12 (7) , 1135
Open AccessDetection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models
Introduction: The long-QT syndrome (LQTS) is the most common ion channelopathy, typically presenting with a prolonged QT interval and clinical symptoms such as syncope or sudden cardiac death. Patients may present with a concealed phenotype making the diagnosis challenging. Correctly diagnosing at-risk patients is pivotal to starting early preventive treatment. Objective: Identification of congenital and often concealed LQTS by utilizing novel deep learning network architectures, which are specifically designed for multichannel time series and therefore particularly suitable for ECG data. Design and Results: A retrospective artificial intelligence (AI)-based analysis was performed using a 12-lead ECG of genetically confirmed LQTS (n = 124), including 41 patients with a concealed LQTS (33%), and validated against a control cohort (n = 161 of patients) without known LQTS or without QT-prolonging drug treatment but any other cardiovascular disease. The performance of a fully convolutional network (FCN) used in prior studies was compared with a different, novel convolutional neural network model (XceptionTime). We found that the XceptionTime model was able to achieve a higher balanced accuracy score (91.8%) than the associated FCN metric (83.6%), indicating improved prediction possibilities of novel AI architectures. The predictive accuracy prevailed independently of age and QTc parameters. Conclusions: In this study, the XceptionTime model outperformed the FCN model for LQTS patients with even better results than in prior studies. Even when a patient cohort with cardiovascular comorbidities is used. AI-based ECG analysis is a promising step for correct LQTS patient identification, especially if common diagnostic measures might be misleading.