BACKGROUND: The rising prevalence of dementia necessitates a scalable solution to cognitive screening. Paper-based cognitive screening examinations are well-validated but minimally scalable. If a digital cognitive screening examination could replicat...
BACKGROUND: In acute neck infections, magnetic resonance imaging (MRI) shows retropharyngeal edema (RPE), which is a prognostic imaging biomarker for a severe course of illness. This study aimed to develop a deep learning-based algorithm for the auto...
PURPOSE: Accurate preoperative diagnosis of parotid gland tumors (PGTs) is crucial for surgical planning since malignant tumors require more extensive excision. Though fine-needle aspiration biopsy is the diagnostic gold standard, its sensitivity in ...
PURPOSE: To identify biomarkers linking molecular mechanisms to macroscale brain changes in major depressive disorder (MDD) by integrating multimodal neuroimaging, transcriptomics, and machine learning.
BACKGROUND: Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. In this study, we aimed to develop predictive models using clinical and neuroimaging data...
The purpose of this experiment was to investigate the effect of robot arm size, movement speed, and degrees of freedom on perceived safety, trust, mental workload, human behaviors, and task performance in a collaborative pick-and-place task. Fifty-si...
OBJECTIVES: The purpose of this study was to automatically segment and quantify the median nerve and carpal arch from ultrasound images using convolutional neural network (CNN).
The simultaneous monitoring of both blood glucose level (BGL) and blood pressure (BP) has rarely been studied directly. The exploitation of physiological interactions between them will advance the learning of either task. However, the lack of availab...
OBJECTIVES: To develop and validate a CT image-based multiple time-series deep learning model for the longitudinal prediction of benign and malignant pulmonary ground-glass nodules (GGNs).
Diagnosing mitochondrial diseases remains challenging because of the heterogeneous symptoms. This study aims to use machine learning to predict mitochondrial diseases from phenotypes to reduce genetic testing costs. This study included patients who u...
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