Can AI predict the progression of autoimmune diseases?

Last year, we discussed how artificial intelligence (AI) could be used to enhance autoimmune disease prediction and treatments through an algorithm called EXPRESSO—EXpression PREdiction with Summary Statistics Only—developed by a research team at Penn State College of Medicine. Penn State researchers have developed a new AI method to predict autoimmune disease progression.

The new method, called Genetic Progression Score (GPS), incorporates data from two sources, large case-control genome-wide association studies (GWAS) and biobanks, to improve the accuracy in identifying those in preclinical stages of an autoimmune disease and to predict the progression of the disease. Individuals with a high GPS score have a high risk of the disease progressing from preclinical to disease stages. 

Dr. Dajiang Liu states the advantage of targeting those with early symptoms or a family history of autoimmune diseases: "We can use machine learning to identify patients with the highest risk for disease and then identify suitable therapeutics that may be able to slow down the progression of the disease."

Not only can GPS help in early detection and intervention, but it can also help prevent irreversible damage to the body. Having a method that can accurately predict disease progression can lead to a more targeted and personalized treatment plan, resulting in improved patient outcomes. GPS may also boost clinical trial recruitment efforts by identifying individuals who are most likely to benefit from new treatments.

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