Arthritis

Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs.

Cycling of biologic or targeted synthetic disease modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA) patients due to non-response is a problem preventing and delaying disease control. We aimed to assess and validate treatment response of b/tsDMARDs among clusters of RA patients identified by deep learning. We clustered RA patients clusters at first-time b/tsDMARD (cohort entry...

Comparison of evaluation metrics of deep learning for imbalanced imaging data in osteoarthritis studies.

PURPOSE: To compare the evaluation metrics for deep learning methods that were developed using imbalanced imaging data in osteoarthritis studies.

Weight-shifting-based robot control system improves the weight-bearing rate and balance ability of the static standing position in hip osteoarthritis patients: a randomized controlled trial focusing on outcomes after total hip arthroplasty.

BACKGROUND: After a total hip arthroplasty (THA), standing and walking balance are greatly affected in the early stages of recovery, so it is importan...

Imaging of early-stage osteoarthritis: the needs and challenges for diagnosis and classification.

In an effort to boost the development of new management strategies for OA, there is currently a shift in focus towards the diagnosis and treatment of ...

Machine and deep learning in inflammatory bowel disease.

PURPOSE OF REVIEW: The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; ho...

Deep learning-based assessment of knee septic arthritis using transformer features in sonographic modalities.

PURPOSE: Septic arthritis is an infectious disease. Conventionally, the diagnosis of septic arthritis can only be based on the identification of causa...

Prediction of total knee replacement using deep learning analysis of knee MRI.

Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to make robust and accurate outcome predictions. Deep ...

Predicting hip-knee-ankle and femorotibial angles from knee radiographs with deep learning.

BACKGROUND: Knee alignment affects the development and surgical treatment of knee osteoarthritis. Automating femorotibial angle (FTA) and hip-knee-ank...

A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints.

Xanthine oxidase inhibitors (XOIs) have been widely studied due to the promising potential as safe and effective therapeutics in hyperuricemia and gou...

DeepNAPSI multi-reader nail psoriasis prediction using deep learning.

Nail psoriasis occurs in about every second psoriasis patient. Both, finger and toe nails can be affected and also severely destroyed. Furthermore, na...

Development of machine learning models based on molecular fingerprints for selection of small molecule inhibitors against JAK2 protein.

Janus kinase 2 (JAK2) is emerging as a potential therapeutic target for many inflammatory diseases such as myeloproliferative disorders (MPD), cancer ...

Osteoarthritis year in review 2022: imaging.

PURPOSE: This narrative review summarizes original research focusing on imaging in osteoarthritis (OA) published between April 1st 2021 and March 31st...

Deep Learning Prediction of Promoter Mutation Status in Thyroid Cancer Using Histologic Images.

objectives: Telomerase reverse transcriptase () promoter mutation, found in a subset of patients with thyroid cancer, is strongly associated with agg...

Cost-Effectiveness of Robot-Assisted Total Knee Arthroplasty: A Markov Decision Analysis.

BACKGROUND: Robot-assisted total knee arthroplasty (rTKA) may improve clinical outcomes for patients who have end-stage osteoarthritis of the knee. Ho...

Detecting hand joint ankylosis and subluxation in radiographic images using deep learning: A step in the development of an automatic radiographic scoring system for joint destruction.

We propose a wrist joint subluxation/ankylosis classification model for an automatic radiographic scoring system for X-ray images. In managing rheumat...

Deep learning applications in osteoarthritis imaging.

Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteo...

Do artificial neural networks love sex? How the combination of artificial neural networks with evolutionary algorithms may help to identify gender influence in rheumatic diseases.

Although medical research has been performed predominantly on men both in preclinical and clinical studies, continuous efforts have been made to overc...

Prediction of knee adduction moment using innovative instrumented insole and deep learning neural networks in healthy female individuals.

BACKGROUND: The knee adduction moment, a biomechanical risk factor of knee osteoarthritis, is typically measured in a gait laboratory with expensive e...

Identification of Glucose Transport Modulators In Vitro and Method for Their Deep Learning Neural Network Behavioral Evaluation in Glucose Transporter 1-Deficient Mice.

Metabolic flux augmentation via glucose transport activation may be desirable in glucose transporter 1 (Glut1) deficiency syndrome (G1D) and dementia,...

Democratization of deep learning for segmenting cartilage from MRIs of human knees: Application to data from the osteoarthritis initiative.

In this study, we aimed to democratize access to convolutional neural networks (CNN) for segmenting cartilage volumes, generating state-of-the-art res...