Accurate detection of arrhythmias on raw electrocardiogram images: An aggregation attention multi-label model for diagnostic assistance.

Background: The low rate of detection of abnormalities has been a major problem with current artificial intelligence-based electrocardiogram diagnostic algorithms, particularly when applied under real-world clinical scenarios.

Methods: We proposed an aggregation attention multilabel electrocardiogram classification model (AA-ECG) that can be applied directly to raw images to identify cardiac abnormalities using image-level annotation only. To develop and validate the model, we conducted a prospective two-site study to build two large-scale real-world datasets of 12-lead electrocardiogram images, annotated by clinical experts in a multilabeled manner.

Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter.

Artificial intelligence is increasingly being used on the clinical electrocardiogram workflows. Few electrocardiograms based on artificial intelligence algorithms have focused on detecting myocardial ischemia using long-term electrocardiogram data. A main reason for this is that interference signals generated from daily activities while wearing the Holter monitor lowered the ability of artificial intelligence to detect myocardial ischemia.

Practical fine-grained learning based anomaly classification for ECG image.

As a widely used vital sign within cardiology, Electrocardiography (ECG) provides the basis for assessing heart function and diagnosing cardiovascular diseases. Automated anomaly detection for ECG plays an important role in improving patient diagnosis efficiency and reducing healthcare costs. Practically, due to the limits of electronics support or the medical system setting, image is a more common format for large-scale ECG storage in most clinical institutions.

FABP3 Deficiency Exacerbates Metabolic Derangement in Cardiac Hypertrophy and Heart Failure via PPARα Pathway.

Cardiac hypertrophy was accompanied by various cardiovascular diseases (CVDs), and due to the high global incidence and mortality of CVDs, it has become increasingly critical to characterize the pathogenesis of cardiac hypertrophy. We aimed to determine the metabolic roles of fatty acid binding protein 3 (FABP3) on transverse aortic constriction (TAC)-induced cardiac hypertrophy. Transverse aortic constriction or Ang II treatment markedly upregulated Fabp3 expression.