Innovative AI Technique for Early Detection of Arthritis and Lupus in At-Risk Individuals
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New Delhi, Jan 8 (NationPress) Artificial intelligence (AI) has the potential to greatly assist in the early identification of autoimmune disorders, including rheumatoid arthritis and lupus, particularly in high-risk patients, according to a recent study.
In individuals with autoimmune conditions, the immune system erroneously targets the body's healthy cells and tissues. Notable examples of these diseases include type 1 diabetes, multiple sclerosis, lupus, and rheumatoid arthritis.
Timely diagnosis is essential as it can enhance treatment options and improve disease management, stated the research team led by experts from the Penn State College of Medicine.
The researchers utilized machine learning, a subset of AI, to create a novel approach that predicts disease progression in individuals exhibiting preclinical symptoms.
These conditions frequently have a preclinical phase, which is marked by subtle symptoms or specific antibodies present in the bloodstream.
The technique, referred to as Genetic Progression Score or GPS, has the capability to forecast the transition from preclinical to full-blown disease stages.
In their research, the team employed GPS to evaluate real-world data, aiming to anticipate the advancement of rheumatoid arthritis and lupus.
When compared to existing prediction models, this new method demonstrated an accuracy increase ranging from 25 to 1,000 percent in identifying mild symptoms likely to escalate to advanced disease stages, according to the findings.
"By focusing on a more pertinent population—those with a family history or exhibiting early symptoms—we can leverage machine learning to pinpoint patients at the highest risk for these diseases," explained Dajiang Liu, a professor at the Penn State College of Medicine. Liu also emphasized that this method could facilitate the identification of appropriate therapeutics capable of slowing disease progression.
Accurate forecasting of disease advancement through GPS can support proactive interventions, targeted monitoring, and customized treatment plans, ultimately enhancing patient outcomes, added Liu.
