BACKGROUND: Parkinson disease (PD) presents diagnostic challenges due to its heterogeneous motor and nonmotor manifestations. Traditional machine learning (ML) approaches have been evaluated on structured clinical variables. However, the diagnostic u...
BACKGROUND: In recent years, the incidence of cognitive diseases has also risen with the significant increase in population aging. Among these diseases, Alzheimer disease constitutes a substantial proportion, placing a high-cost burden on health care...
Biomedical physics & engineering express
Jan 15, 2026
Objective.Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal communication. This study explores the feasibility of decoding pain from EEG using explainable deep learni...
Inflammation research : official journal of the European Histamine Research Society ... [et al.]
Jan 14, 2026
OBJECTIVE: To develop an interpretable prognostic prediction model for autoimmune encephalitis (AE) using immunological indicators and to investigate the potential role of nucleophosmin (NPM1) in disease pathogenesis through multi-omics approaches.
Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machine learning methods struggle to address the challenge of early DVT diagnosis due to their inability ...
UNLABELLED: Childhood obesity is the main driver of early metabolic risk, predisposing to cardiovascular disease (CVD) and type 2 diabetes (T2D), which cause millions of deaths worldwide. Their progression is influenced by biological, behavioral, and...
. Fetal and maternal health during pregnancy can be monitored with sensors such as Doppler or scalp fetal ECG. This study focuses on single-channel dry electrode maternal abdominal ECG () to extract fetal heart rate () using a low-complexity algorith...
BACKGROUND: The referral process between healthcare services can be complex, especially in psychiatry, leading to significant delays and 'hidden waiting lists'. Digital approaches may be helpful. The CHRONOSIG (CHRONOlogical SIGnature) project aims t...
The purpose of this study was to compare manual assessment of corneal nerve fiber length (CNFL) and dendritic cell (DC) density with an automated assessment method utilizing deep learning segmentation to perform rule-based density estimation. Corneal...
BACKGROUND/AIMS: To evaluate the performance of an artificial intelligence (AI) model for detecting and monitoring microbial keratitis (MK) using anterior segment optical coherence tomography (AS-OCT).
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