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Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms.
JACC Asia
January 2025
Department of Frontier Cardiovascular Science, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: Heart failure should be diagnosed as early as possible. Although deep learning models can predict one or more echocardiographic findings from electrocardiograms (ECGs), such analyses are not comprehensive.
Objectives: This study aimed to develop a deep learning model for comprehensive prediction of echocardiographic findings from ECGs.
Objective: Fluoropyrimidine chemotherapy is a first-line treatment for many gastrointestinal (GI) cancers, however, cardiotoxicity concerns may limit administration in patients with pre-existing cardiovascular disease (CVD). This study investigated the association of pre-existing CVD with use of fluoropyrimidine chemotherapy in tumour-eligible GI cancer patients.
Methods And Analysis: National cancer registry data from the Virtual Cardio-Oncology Research Initiative from England between 2014 and 2018 was used to identify GI cancer patients eligible to receive fluoropyrimidine chemotherapy.
Probabilistic learning of the Purkinje network from the electrocardiogram.
Med Image Anal
January 2025
Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Chile. Electronic address:
The identification of the Purkinje conduction system in the heart is a challenging task, yet essential for a correct definition of cardiac digital twins for precision cardiology. Here, we propose a probabilistic approach for identifying the Purkinje network from non-invasive clinical data such as the standard electrocardiogram (ECG). We use cardiac imaging to build an anatomically accurate model of the ventricles; we algorithmically generate a rule-based Purkinje network tailored to the anatomy; we simulate physiological electrocardiograms with a fast model; we identify the geometrical and electrical parameters of the Purkinje-ECG model with Bayesian optimization and approximate Bayesian computation.
View Article and Find Full Text PDFClozapine for Treatment-Resistant Disruptive Behaviors in Youths With Autism Spectrum Disorder Aged 10-17 Years: Protocol for an Open-Label Trial.
JMIR Res Protoc
January 2025
Graduate Program of Psychiatry and Behavioral Sciences, Department of Psychiatry, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition emerging in early childhood, characterized by core features such as sociocommunicative deficits and repetitive, rigid behaviors, interests, and activities. In addition to these, disruptive behaviors (DB), including aggression, self-injury, and severe tantrums, are frequently observed in pediatric patients with ASD. The atypical antipsychotics risperidone and aripiprazole, currently the only Food and Drug Administration-approved treatments for severe DB in patients with ASD, often encounter therapeutic failure or intolerance.
View Article and Find Full Text PDFImproving myocardial infarction diagnosis with Siamese network-based ECG analysis.
PLoS One
January 2025
School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India.
Background: Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood flow. The leading cause of death for middle-aged and older people worldwide is myocardial infarction (MI), which is difficult to diagnose because it has no symptoms. Clinicians must evaluate electrocardiography (ECG) signals to diagnose MI, which is difficult and prone to observer bias.
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