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Gout

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Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method.

Frontiers in immunology
BACKGROUND: The relationship between systemic inflammatory index (SII), sex steroid hormones, dietary antioxidants (DA), and gout has not been determined. We aim to develop a reliable and interpretable machine learning (ML) model that links SII, sex ...

Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism-related outcomes. Early detection and prognosis prediction for HUA and gout are crucial...

A machine learning-based prediction model for gout in hyperuricemics: a nationwide cohort study.

Rheumatology (Oxford, England)
OBJECTIVE: To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout.

Large language models for accurate disease detection in electronic health records: the examples of crystal arthropathies.

RMD open
OBJECTIVES: We propose and test a framework to detect disease diagnosis using a recent large language model (LLM), Meta's Llama-3-8B, on French-language electronic health record (EHR) documents. Specifically, it focuses on detecting gout ('goutte' in...

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

European journal of radiology
OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlanations (SHAP) for model interpretation.

Thinking Like Sonographers: Human-Centered CNN Models for Gout Diagnosis From Musculoskeletal Ultrasound.

IEEE transactions on bio-medical engineering
We explore the potential of deep convolutional neural network (CNN) models for differential diagnosis of gout from musculoskeletal ultrasound (MSKUS). Our exhaustive study of state-of-the-art (SOTA) CNN image classification models for this problem re...

Exploring the Impact of PA and Sedentary Behavior on Gout Risk in Hyperuricemia: Insights From Machine Learning and SHAP Analysis.

International journal of rheumatic diseases
BACKGROUND: Individuals with hyperuricemia (HUA) are widely recognized as being at increased risk for gout. This study aimed to investigate how physical activity (PA) duration and sedentary duration impact gout risk in individuals with HUA and to dev...

An objective diagnosis of gout and calcium pyrophosphate deposition disease with machine learning of Raman spectra acquired in a point-of-care setting.

Rheumatology (Oxford, England)
OBJECTIVE: Raman spectroscopy is proposed as a next-generation method for the identification of monosodium urate (MSU) and calcium pyrophosphate (CPP) crystals in synovial fluid. As the interpretation of Raman spectra requires specific expertise, the...

An artificial intelligence-based gout management system reduced chronic kidney disease incident and improved target serum urate achievement.

Rheumatology (Oxford, England)
OBJECTIVES: Stage ≥3 chronic kidney disease (CKD) affects ∼25% of people with gout. The effects of urate-lowering therapy (ULT) on CKD incidence and progression have remained inconclusive. Here, we assessed the impact of a gout ULT clinic interventio...