Journal of neuroengineering and rehabilitation
Apr 9, 2025
BACKGROUNDS: In recent years, numerous robotic devices, together with allied technologies, have been developed to support rehabilitation, both in research settings and industry. Although robotic-assisted rehabilitation and related technologies hold s...
BACKGROUND: Gastric stromal tumors (GSTs) and gastric leiomyomas (GLs) represent the primary subtypes of gastric submucosal tumors (SMTs) characterized by distinct biological characteristics and treatment modalities. The accurate differentiation betw...
BACKGROUND: Allergic rhinitis is a common disease that can affect the health of patients and bring huge social and economic burdens. In this study, we developed a model to predict the incidence rate of allergic rhinitis so as to provide accurate info...
UNLABELLED: This study evaluates the role of ChatGPT in osteoporosis management, demonstrating 91% diagnostic accuracy and significantly faster response times compared to clinicians. The findings highlight the potential for artificial intelligence (A...
To explore the value of applying the MRI-based radiomic nomogram for predicting lymph node metastasis (LNM) in rectal cancer (RC). This retrospective analysis used data from 430 patients with RC from two medical centers. The patients were categorized...
Many people suffer from Parkinson's disease globally, a complicated neurological condition caused by the deficiency of dopamine, an organic chemical responsible for regulating movement in individuals. Patients with Parkinson face muscle stiffness or ...
BACKGROUND: Valuable insights gathered by clinicians during their inquiries and documented in textual reports are often unavailable in the structured data recorded in electronic health records (EHRs).
BACKGROUND: Postoperative acute kidney injury (AKI) is a significant risk associated with surgeries under general anesthesia, often leading to increased mortality and morbidity. Existing predictive models for postoperative AKI are usually limited to ...
OBJECTIVE: This study aimed to validate and optimize a machine learning algorithm for accurately predicting low-density lipoprotein cholesterol (LDL-C) levels, addressing limitations of traditional formulas, particularly in hypertriglyceridemia.
BACKGROUND: Head and neck cancer (HNC) becomes a vital global health burden. Accurate assessment of the disease burden plays an essential role in setting health priorities and guiding decision-making.