Arthritis

Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment.

A neural network was trained to generate synthetic images of severe and moderate psoriatic plaques, after being trained on 375 photographs of patients with psoriasis taken in a clinical setting. A latent w-space vector was identified that allowed the degree of severity of the psoriasis in the generated images to be modified. A second latent w-space vector was identified that allowed the size of th...

Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning.

Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and ...

An interpretable machine learning approach for detecting psoriatic arthritis in a UK primary care psoriasis cohort using electronic health records from the Clinical Practice Research Datalink.

OBJECTIVES: Develop an interpretable machine learning model to detect patients with newly diagnosed psoriatic arthritis (PsA) in a cohort of psoriasis...

Identify the potential target of efferocytosis in knee osteoarthritis synovial tissue: a bioinformatics and machine learning-based study.

INTRODUCTION: Knee osteoarthritis (KOA) is a degenerative joint disease characterized by the progressive deterioration of cartilage and synovial infla...

Comparative analysis of machine learning and deep learning algorithms for knee arthritis detection using YOLOv8 models.

Knee arthritis is a prevalent joint condition that affects many people worldwide. Early detection and appropriate treatment are essential to slow the ...

Machine Learning-Assisted Chemical Tongues Based on Dual-channel Inclusion Complexes for Rapid Identification of Nonsteroidal Anti-inflammatory Drugs in Food.

The improper application of nonsteroidal anti-inflammatory drugs (NSAIDs) presents significant health hazards via vector food contamination. A critica...

A new era of psoriasis treatment: Drug repurposing through the lens of nanotechnology and machine learning.

Psoriasis is a persistent inflammatory skin disorder characterized by hyper-proliferation and abnormal epidermal differentiation. Conventional treatme...

Finger-aware Artificial Neural Network for predicting arthritis in Patients with hand pain.

Arthritis is an inflammatory condition associated with joint damage, the incidence of which is increasing worldwide. In severe cases, arthritis can re...

Development of a Wearable Sleeve-Based System Combining Polymer Optical Fiber Sensors and an LSTM Network for Estimating Knee Kinematics.

This study presents a novel wearable solution integrating Polymer Optical Fiber (POF) sensors into a knee sleeve to monitor knee flexion/extension (F/...

Deep learning-based clustering for endotyping and post-arthroplasty response classification using knee osteoarthritis multiomic data.

OBJECTIVES: Primary knee osteoarthritis (KOA) is a heterogeneous disease with clinical and molecular contributors. Biofluids contain microRNAs and met...

Hybrid attention-CNN model for classification of gait abnormalities using EMG scalogram images.

This research aimed to develop an algorithm for classifying scalogram images generated from electromyography data of patients with Rheumatoid Arthriti...

Automatic Joint Lesion Detection by enhancing local feature interaction.

Recently, deep learning models have demonstrated impressive performance in Automatic Joint Lesion Detection (AJLD), yet balancing accuracy and efficie...

Biopsychosocial based machine learning models predict patient improvement after total knee arthroplasty.

Total knee arthroplasty (TKA) is an effective treatment for end stage osteoarthritis. However, biopsychosocial features are not routinely considered i...

Generating synthetic past and future states of Knee Osteoarthritis radiographs using Cycle-Consistent Generative Adversarial Neural Networks.

Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, ext...

Risk prediction of functional disability among middle-aged and older adults with arthritis: A nationwide cross-sectional study using interpretable machine learning.

BACKGROUND: Arthritis is a common chronic disease among middle-aged and older adults and is strongly related to functional decline.

Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.

OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlan...

Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.

PURPOSE: Distinguishing between Osteonecrosis of the femoral head (ONFH) and Osteoarthritis (OA) can be subjective and vary between users with differe...

Identification of WDR74 and TNFRSF12A as biomarkers for early osteoarthritis using machine learning and immunohistochemistry.

BACKGROUND: Osteoarthritis (OA) is a chronic joint condition that causes pain, limited mobility, and reduced quality of life, posing a threat to healt...

Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis.

The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual s...

Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.

Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence...