AIMC Topic: Scleroderma, Systemic

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Letter to editor 'Prediction of prognosis in patients with systemic sclerosis based on a machine-learning model'.

Clinical rheumatology Aug 13, 2024
Humans Machine Learning Prognosis Scleroderma, Systemic
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Prediction of prognosis in patients with systemic sclerosis based on a machine-learning model.

Clinical rheumatology Jun 28, 2024
OBJECTIVE: The clinical manifestations of systemic sclerosis (SSc) are highly variable, resulting in varied outcomes and complications. Diverse fibrosis of the skin and internal organs, vasculopathy, and dysregulated immune system lead to poor and va...
Scleroderma, Systemic Nomograms Survival Analysis Proportional Hazards Models Female Humans Adult Risk Factors Machine Learning Male Aged Prognosis Middle Aged China
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The state of artificial intelligence for systemic dermatoses: Background and applications for psoriasis, systemic sclerosis, and much more.

Clinics in dermatology Jun 21, 2024
Artificial intelligence (AI) has been steadily integrated into dermatology, with AI platforms already attempting to identify skin cancers and diagnose benign versus malignant lesions. Although not as widely known, AI programs have also been utilized ...
Humans Artificial Intelligence Algorithms Psoriasis Dermatomyositis Scleroderma, Systemic
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Expert-Level Distinction of Systemic Sclerosis from Hand Photographs Using Deep Convolutional Neural Networks.

The Journal of investigative dermatology Apr 7, 2021
Female Adult Male Middle Aged Humans Neural Networks, Computer Hand Scleroderma, Systemic Photography
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Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis.

Biomedical engineering online Mar 25, 2021
INTRODUCTION: The use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis...
Spirometry Biometry Scleroderma, Systemic Oscillometry Middle Aged Diagnosis, Computer-Assisted Adolescent Respiration Disorders Algorithms Computers Female Adult Male Aged Young Adult Humans Artificial Intelligence Machine Learning
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Lung Shrinkage: An Additional CT Marker in the Follow-up of Fibrotic Interstitial Lung Disease.

Radiology Oct 20, 2020
Humans Tomography, X-Ray Computed Lung Diseases, Interstitial Lung Scleroderma, Systemic Deep Learning Follow-Up Studies
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Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.

Radiology Oct 20, 2020
Background Longitudinal follow-up of interstitial lung diseases (ILDs) at CT mainly relies on the evaluation of the extent of ILD, without accounting for lung shrinkage. Purpose To develop a deep learning-based method to depict worsening of ILD based...
Radiographic Image Interpretation, Computer-Assisted Lung Diseases, Interstitial Scleroderma, Systemic Tomography, X-Ray Computed Female Male Deep Learning Longitudinal Studies Middle Aged Lung Humans Retrospective Studies
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High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.

Arthritis research & therapy Mar 14, 2020
BACKGROUND: Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRS...
Azo Compounds Scleroderma, Systemic Eosine Yellowish-(YS) Methyl Green Scleroderma, Localized Female Humans Principal Component Analysis Adult Biopsy Male Neural Networks, Computer Severity of Illness Index Skin Middle Aged Algorithms Deep Learning Cohort Studies
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Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.

Arthritis research & therapy Dec 30, 2019
BACKGROUND: Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We devel...
Middle Aged International Classification of Diseases Scleroderma, Systemic Adult Algorithms Sensitivity and Specificity Humans Databases, Factual Male Machine Learning Electronic Health Records Aged Female Aged, 80 and over Reproducibility of Results
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Using Machine Learning to Molecularly Classify Systemic Sclerosis Patients.

Arthritis & rheumatology (Hoboken, N.J.) Sep 9, 2019
Scleroderma, Systemic Humans Machine Learning Image Processing, Computer-Assisted
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