Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography
(ECG) still represents the benchmark approach for identifying cardiac irregularities …

Ultrasound (US) is generally preferred because it is of low-cost, safe, and non-invasive. US
image segmentation is crucial in image analysis. Recently, deep learning-based methods …

Abstract Segmentation of abdominal Computed Tomography (CT) scan is essential for
analyzing, diagnosing, and treating visceral organ diseases (eg, hepatocellular carcinoma) …

Owing to limited drug testing possibilities in pregnant population, the development of
computational algorithms is crucial to predict the fate of drugs in the placental barrier; it …

Clinical imaging (eg, magnetic resonance imaging and computed tomography) is a crucial
adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate …

Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …

Computational drug repurposing is an efficient method to utilize existing knowledge for
understanding and predicting their effect on neurological diseases. The ability of a molecule …

Elastography Ultrasound provides elasticity information of the tissues, which is crucial for
understanding the density and texture, allowing for the diagnosis of different medical …

Background Percutaneous thermal ablation has become the preferred therapeutic treatment
option for liver cancers that cannot be resected. Since ablative zone tissue changes over …

Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity
produced by the contraction and relaxation of the cardiac muscles. It has been established …