Rheumatology

Ontology-based integration and querying of heterogeneous rare disease data sources - POLVAS perspective.

The integration of rare disease medical databases belonging to different countries is an important problem, as a large number of observations are required for reliable statistical inference of patient data in order to facilitate clinical research. Such integration of national registry data, which requires harmonization of the heterogeneous data sets into a unified view, is facilitated in the Europ...

A Machine Learning-Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults.

Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning-based prediction model to predict the fall risk category among community-dwelling older adults, leading to earlier intervention and better outcomes. Three prediction models (logistic regression, random forest, and naive Bayes) were constructed and evaluated. A total...

anti-cancer activity of siddha metallo-mineral formulation from Thanga uram with Hela cell lines.

The second most common malignant tumour in women worldwide, cervical cancer seriously jeopardizes their health. It has been established that a high-ri...

Automatic classification of HEp-2 specimens by explainable deep learning and Jensen-Shannon reliability index.

The Anti-Nuclear Antibodies (ANA) test using Human Epithelial type 2 (HEp-2) cells in the Indirect Immuno-Fluorescence (IIF) assay protocol is conside...

A deep learning model based on the BERT pre-trained model to predict the antiproliferative activity of anti-cancer chemical compounds.

Identifying new compounds with minimal side effects to enhance patients' quality of life is the ultimate goal of drug discovery. Due to the expensive ...

RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping.

Cardiac T1 mapping can evaluate various clinical symptoms of myocardial tissue. However, there is currently a lack of effective, robust, and efficient...

Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach.

OBJECTIVES: In axial spondyloarthritis (axSpA), early diagnosis is crucial, but diagnostic delay remains long and diagnostic criteria do not exist. We...

Current update on the neurological manifestations of long COVID: more questions than answers.

Since the outbreak of the COVID-19 pandemic, there has been a global surge in patients presenting with prolonged or late-onset debilitating sequelae o...

Leveraging AI models for lesion detection in osteonecrosis of the femoral head and T1-weighted MRI generation from radiographs.

This study emphasizes the importance of early detection of osteonecrosis of the femoral head (ONFH) in young patients on long-term glucocorticoid ther...

Improved facial emotion recognition model based on a novel deep convolutional structure.

Facial Emotion Recognition (FER) is a very challenging task due to the varying nature of facial expressions, occlusions, illumination, pose variations...

Analysis of the relationships between interferon-stimulated genes and anti-SSA/Ro 60 antibodies in primary Sjögren's syndrome patients via multiomics and machine learning methods.

BACKGROUND: Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease characterized by lymphocyte infiltration of the exocrine glands....

Artificial intelligence meets the world experts; updates and novel therapies in autoimmunity - The 14th international congress on autoimmunity 2024 (AUTO14), Ljubljana.

The bi-annual international congress on autoimmunity is a huge opportunity for the medical community to discuss the latest updates in the field. Durin...

A Multi-task learning U-Net model for end-to-end HEp-2 cell image analysis.

Antinuclear Antibody (ANA) testing is pivotal to help diagnose patients with a suspected autoimmune disease. The Indirect Immunofluorescence (IIF) mic...

Machine learning identifies cytokine signatures of disease severity and autoantibody profiles in systemic lupus erythematosus - a pilot study.

Disrupted cytokine networks and autoantibodies play an important role in the pathogenesis of systemic lupus erythematosus. However, conflicting report...

Explainable Deep Learning Approaches for Risk Screening of Periodontitis.

Several pieces of evidence have been reported regarding the association between periodontitis and systemic diseases. Despite the emphasized significan...

Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis.

BACKGROUND: Kawasaki disease (KD) is an acute pediatric vasculitis that can lead to coronary artery aneurysms and severe cardiovascular complications,...

Machine learning identifies immune-based biomarkers that predict efficacy of anti-angiogenesis-based therapies in advanced lung cancer.

BACKGROUND: The anti-angiogenic drugs showed remarkable efficacy in the treatment of lung cancer. Nonetheless, the potential roles of the intra-tumora...

Anti-VEGF treatment outcome prediction based on optical coherence tomography images in neovascular age-related macular degeneration using a deep neural network.

Age-related macular degeneration (AMD) is a major cause of blindness in developed countries, and the number of affected patients is increasing worldwi...

Machine learning for precision diagnostics of autoimmunity.

Early and accurate diagnosis is crucial to prevent disease development and define therapeutic strategies. Due to predominantly unspecific symptoms, di...

Learning to predict perceptual visibility of rendering deterioration in computer games.

Contemporary computer gaming affords players the agency to manually tailor rendering settings, a capability crucial for optimizing computational deman...