The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking th...
CorneAI, a deep learning model designed for diagnosing cataracts and corneal diseases, was assessed for its impact on ophthalmologists' diagnostic accuracy. In the study, 40 ophthalmologists (20 specialists and 20 residents) classified 100 images, in...
Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large om...
Medical image segmentation plays a crucial role in addressing emerging healthcare challenges. Although several impressive deep learning architectures based on convolutional neural networks (CNNs) and Transformers have recently demonstrated remarkable...
Intestinal organoids are indispensable tools for exploring intestinal disorders. Deep learning methodologies are often employed in morphological analysis to evaluate the condition of these organoids. Nonetheless, prevailing analytical techniques face...
This study aims to apply a multi-modal approach of the deep learning method for survival prediction in patients with non-small-cell lung cancer (NSCLC) using CT-based radiomics. We utilized two public data sets from the Cancer Imaging Archive (TCIA) ...
IEEE transactions on biomedical circuits and systems
Feb 11, 2025
Epilepsy is a globally distributed chronic neurological disorder that may pose a threat to life without warning. Therefore, the use of wearable devices for real-time detection and treatment of epilepsy is crucial. Additionally, personalizing disease ...
Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain's functioning prominently. However,...
In many deep learning tasks, it is assumed that the data used in the training process is sampled from the same distribution. However, this may not be accurate for data collected from different contexts or during different periods. For instance, the t...
Respiratory toxicity of chemicals is a common clinical and environmental health concern. Currently, most in silico prediction models for chemical respiratory toxicity are often based on a single or vague toxicity endpoint, and machine learning models...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.