Previous models of depression outcomes have been limited by symptom heterogeneity within populations. This study conducted a retrospective analysis using latent growth mixture models to identify heterogeneous trajectories within a clinical population...
PURPOSE: Traditional vocal fold pathology recognition typically requires expertise of laryngologists and advanced instruments, primarily through direct visualization. This study aims to augment this conventional paradigm by introducing a parallel dia...
International journal of biological macromolecules
Dec 4, 2024
Although recent advancements have shed light on the crucial role of coordinated evolution among cell subpopulations in influencing disease progression, the full potential of these insights has not yet been fully harnessed in the clinical application ...
OBJECTIVE: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.
The prediction of brain cancer occurrence and risk assessment of brain hemorrhage using a hybrid deep learning (DL) technique is a critical area of research in medical imaging analysis. One prominent challenge in this field is the accurate identifica...
Neoadjuvant chemotherapy assessment is imperative for prognostication and clinical management of locally advanced gastric cancer. We propose an incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framew...
Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characteriz...
BMC medical informatics and decision making
Dec 4, 2024
BACKGROUND: The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This st...
OBJECTIVE: We aimed to develop an AI-based model that uses a portable electronic oral endoscope to capture intraoral images of patients for the detection of oral cancer.
BACKGROUND: Preterm birth (PTB) is a significant cause of neonatal mortality and long-term health issues. Accurate prediction and timely prevention of PTB are essential for reducing associated child mortality and morbidity. Traditional predictive met...
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