Arthritis

Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative.

Background The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. Materials and Methods ...

Serum adipokines/related inflammatory factors and ratios as predictors of infrapatellar fat pad volume in osteoarthritis: Applying comprehensive machine learning approaches.

OBJECTIVE: The infrapatellar fat pad (IPFP) has been associated with knee osteoarthritis onset and progression. This study uses machine learning (ML) approaches to predict serum levels of some adipokines/related inflammatory factors and their ratios on knee IPFP volume of osteoarthritis patients.

Applying cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients.

OBJECTIVES: We have previously shown that neural network technology can be used for scoring arthritis disease activity in ultrasound images from rheum...

S100 proteins, cytokines, and chemokines as tear biomarkers in children with juvenile idiopathic arthritis-associated uveitis.

PURPOSE: Biomarkers for juvenile idiopathic arthritis-associated uveitis (JIA-U) are needed. We aimed to measure inflammatory biomarkers in tears as a...

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy b...

Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches.

In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in ...

Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes.

PURPOSE OF REVIEW: The propose of this viewpoint is to improve or facilitate the clinical decision-making in the management/treatment strategies of ar...

What's new in IBD therapy: An "omics network" approach.

The industrial revolution that began in the late 1800s has resulted in dramatic changes in the environment, human lifestyle, dietary habits, social st...

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the po...

Deep learning-based fully automatic segmentation of wrist cartilage in MR images.

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation fr...

Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography.

OBJECTIVE: To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral sa...

A review on segmentation of knee articular cartilage: from conventional methods towards deep learning.

In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) ...

Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images.

Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testin...

Expression Profile of Osteoclasts Following the Stimulation With Interleukin-23 in Mice.

OBJECTIVES: This study aims to analyze the expression profile of osteoclasts (OCs) following the stimulation with interleukin 23 (IL-23) in mice, whic...

Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images.

Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as ex...

Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.

Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computatio...

Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods.

OBJECTIVE: The incidence of osteoarthritis is gradually increasing in public due to aging and increase in obesity. Various imaging methods are used in...

Interpretable Artificial Intelligence: Why and When.

The purpose of this article is to discuss the problem of interpretability of artificial intelligence (AI) and highlight the need for continuing scien...

Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs.

Background A multitask deep learning model might be useful in large epidemiologic studies wherein detailed structural assessment of osteoarthritis sti...

The Use of Artificial Intelligence in the Evaluation of Knee Pathology.

Artificial intelligence (AI) holds the potential to revolutionize the field of radiology by increasing the efficiency and accuracy of both interpretiv...