The advancement of the Internet of Medical Things (IoMT) has revolutionized data acquisition and processing in critical care settings. Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient c...
Chronic kidney disease is a persistent ailment marked by the gradual decline of kidney function. Its classification primarily relies on the estimated glomerular filtration rate and the existence of kidney damage. The kidney disease improving global o...
This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. The novelty of this work lies in ...
Intraperitoneal hernia is an acute abdominal disease, with complex imaging features and variable clinical manifestations that challenge surgeons and emergency physicians in early disease assessment and streamlined diagnosis and treatment procedures. ...
Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant focal epilepsy. However, it remains unclear whether FC-based metrics provide additional value beyond ...
Atherosclerosis is a chronic inflammatory disease, this study aims to investigate the immune landscape in carotid atherosclerotic plaque formation and explore diagnostic biomarkers of lactylation-associated genes, so as to gain new insights into unde...
The radiological dosimetric parameters and clinical features were screened by machine learning to construct a prediction model for the short-term efficacy of locally advanced Nasopharyngeal Carcinoma (LANPC). Patients diagnosed with Nasopharyngeal Ca...
Emotion recognition in text is a fundamental task in natural language processing, underpinning applications such as sentiment analysis, mental health monitoring, and content moderation. Although transformer-based models like RoBERTa have advanced con...
Liver cancer, especially hepatocellular carcinoma (HCC), remains one of the most fatal cancers globally, emphasizing the critical need for accurate tumor segmentation to enable timely diagnosis and effective treatment planning. Traditional imaging te...
In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task of classifying common diseases in tea leaves, leveraging advanced image analysis techniques. Our model is distinguished by its complex multi-layer arc...