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The application of deep learning in early enamel demineralization detection.

PeerJ
OBJECTIVE: The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces.

Predicting noncontact injuries of professional football players using machine learning.

PloS one
Noncontact injuries are prevalent among professional football players. Yet, most research on this topic is retrospective, focusing solely on statistical correlations between Global Positioning System (GPS) metrics and injury occurrence, overlooking t...

Application of machine learning in predicting postoperative arrhythmia following transcatheter closure of perimembranous ventricular septal defects.

Kardiologia polska
BACKGROUND: Arrhythmia is a frequent complication following transcatheter device closure of perimembranous ventricular septal defects (pmVSD). However, there is currently a lack of a convenient tool for predicting postoperative arrhythmia.

Spatially-Constrained and -Unconstrained Bi-Graph Interaction Network for Multi-Organ Pathology Image Classification.

IEEE transactions on medical imaging
In computational pathology, graphs have shown to be promising for pathology image analysis. There exist various graph structures that can discover differing features of pathology images. However, the combination and interaction between differing grap...

Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.

IEEE transactions on medical imaging
The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all observations and longitudinal trajectories within individuals. However, characterizing these traject...

Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.

IEEE transactions on medical imaging
The interconnection between brain regions in neurological disease encodes vital information for the advancement of biomarkers and diagnostics. Although graph convolutional networks are widely applied for discovering brain connection patterns that poi...

Iodine Density of Lymphoma, Metastatic SCCA, and Normal Cervical lymph nodes: A Comparative Analysis Based on DLSCT.

F1000Research
OBJECTIVE: To compare iodine density (ID) and contrast-enhanced attenuation value (CEAV) from dual-layer spectral computed tomography (DLSCT) scans of lymphomatous, metastatic squamous cell carcinoma (SCCA), and normal cervical lymph nodes.

Towards the automatic detection of activities of daily living using eye-movement and accelerometer data with neural networks.

Computers in biology and medicine
Early diagnosis of neurodegenerative diseases, such as Alzheimer's disease, improves treatment and care outcomes for patients. Early signs of cognitive decline can be detected using functional scales, which are written records completed by a clinicia...

Identifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.

The journal of prevention of Alzheimer's disease
BACKGROUND: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is c...

Predicting working alliance in psychotherapy: A multi-modal machine learning approach.

Psychotherapy research : journal of the Society for Psychotherapy Research
OBJECTIVE: Session-by-session tracking of the working alliance enables clinicians to detect alliance deterioration and intervene accordingly, which has shown to improve treatment outcome, and reduce dropout. Despite this, regular use of alliance self...