AIMC Topic: Scleroderma, Systemic
Clear Filters Showing 21 to 29 of 29 articles
A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets.
Arthritis & rheumatology (Hoboken, N.J.)
Sep 2, 2019
OBJECTIVE: High-throughput gene expression profiling of tissue samples from patients with systemic sclerosis (SSc) has identified 4 "intrinsic" gene expression subsets: inflammatory, fibroproliferative, normal-like, and limited. Prior methods require...
The role of klotho in systemic sclerosis.
Reumatismo
Dec 21, 2017
The aim was to evaluate the role of klotho in the pathogenesis of systemic sclerosis (SSc), through the measurement of its serum concentration in SSc patients compared to healthy controls, and to assess the association with cutaneous and visceral inv...
Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.
Clinical transplantation
Jul 1, 2025
INTRODUCTION: Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic sclerosis (SSc) remains especially underrepresented in research.
Identification of biomarkers and immune infiltration associated with sexes in SSc: a bioinformatics and machine learning.
Rheumatology (Oxford, England)
Jun 1, 2025
OBJECTIVE: This study aimed to identify key candidate genes associated with the sexes of patients with SSc.
CAPI-Detect: machine learning in capillaroscopy reveals new variables influencing diagnosis.
Rheumatology (Oxford, England)
Jun 1, 2025
OBJECTIVES: Nailfold videocapillaroscopy (NVC) is the gold standard for diagnosing SSc and differentiating primary from secondary RP. The CAPI-Score algorithm, designed for simplicity, classifies capillaroscopy scleroderma patterns (CSPs) using a lim...
Machine learning-driven immunophenotypic stratification of mixed connective tissue disease, corroborating the clinical heterogeneity.
Rheumatology (Oxford, England)
Mar 1, 2025
OBJECTIVE: The objective of this study was to stratify patients with MCTD, based on their immunophenotype.
[An artificial neural network diagnostic model for scleroderma and immune cell infiltration analysis based on mitochondria-associated genes].
Nan fang yi ke da xue xue bao = Journal of Southern Medical University
May 20, 2024
OBJECTIVE: To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.
A deep learning system for quantitative assessment of microvascular abnormalities in nailfold capillary images.
Rheumatology (Oxford, England)
Jun 1, 2023
OBJECTIVES: Nailfold capillaroscopy is key to timely diagnosis of SSc, but is often not used in rheumatology clinics because the images are difficult to interpret. We aimed to develop and validate a fully automated image analysis system to fill this ...
Use of artificial intelligence in imaging in rheumatology - current status and future perspectives.
RMD open
Jan 1, 2020
After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock...
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