With Industry 4.0, big data and artificial intelligence have become paramount in the field of medicine. Electronic health records, the primary source of medical data, are not collected for research purposes but represent real-world data; therefore, t...
International journal of neural systems
Sep 30, 2024
With the rapid advancement of deep learning, computer-aided diagnosis and treatment have become crucial in medicine. UNet is a widely used architecture for medical image segmentation, and various methods for improving UNet have been extensively explo...
Working memory is vital for short-term information processing. Binaural beats can enhance working memory by improving attention and memory consolidation through neural synchronization. However, individual differences in cognitive and neuronal functio...
BACKGROUND: Crohn's disease is a complex genetic disease that involves chronic gastrointestinal inflammation and results from a complex set of genetic, environmental, and immunological factors. By analyzing data from the human microbiome, genetic inf...
BACKGROUND: Tick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their impact on human health requires models covering large geographic areas and considering both the abio...
International journal of neural systems
Sep 30, 2024
A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis and treatment of epilepsy. Aiming to address this issue, a novel lightweight model called Convolutional Ne...
AIM: In this research, we aimed to develop a model for the accurate prediction of gastric cancer based on H&E findings combined with machine learning pathomics.
Developing technology to assist medical experts in their everyday decision-making is currently a hot topic in the field of Artificial Intelligence (AI). This is specially true within the framework of Evidence-Based Medicine (EBM), where the aim is to...
Structural and functional brain networks are generated from two scan sequences of magnetic resonance imaging data, which can provide different perspectives for describing pathological changes caused by brain diseases. Recent studies found that fusing...
The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning models has yielded promising outcomes in discerning phenotypic characteristics and enhancing disease ...
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