Wearable robotic devices have become increasingly prevalent in both occupational and rehabilitative settings, yet their widespread adoption remains inhibited by usability barriers related to comfort, restriction, and noticeable functional benefits. A...
Artificial intelligence has emerged as a transformative tool in healthcare, offering capabilities such as early diagnosis, personalised treatment, and real-time patient monitoring. In the context of rheumatoid arthritis, a chronic autoimmune disease...
BACKGROUND: Deep learning(DL) models can improve significantly discrimination of lymph node metastasis(LNM) of pancreatic ductal adenocarcinoma(PDAC), but have not been systematically assessed.
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: 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...
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...
Through conversation, humans engage in a complex process of alternating speech production and comprehension to communicate. The neural mechanisms that underlie these complementary processes through which information is precisely conveyed by language,...
BACKGROUND: As artificial intelligence (AI) permeates the current society, the young generation is becoming increasingly accustomed to using digital solutions. AI-based medication counseling services may help people take medications more accurately a...
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).
The increasing availability of wearable device data provides an opportunity for developing personalized models for health monitoring and condition prediction. Unlike conventional approaches that rely on pooled data from diverse individuals, our study...