It's highly recognized that digitized healthcare technologies present numerous possibilities and opportunities for reducing human errors, improving clinical outcomes, tracking data over time, etc. Due to the ability to analyze vast amounts of data quickly and in detail, artificial intelligence (AI) methods, from machine learning to deep learning, are taking on more and more crucial functions in numerous healthcare domains, especially in identifying the diagnosis of different types of diseases.
Thriving digitized healthcare technologies enable researchers and technicians from University College Dublin to combine proteomics, the large-scale study of proteins, with data science and machine learning analytics, to deliver highly specific and personalized diagnostic information for various diseases. Their strategies are based on the concept that personalized medicine is the general trend of future healthcare which allows clinicians to tailor treatment for the individual. Hopefully, targeting applications of protein biomarkers in clinical diagnostics, this combined digitized solution would advance such a healthcare revolution by advanced blood test pipelines for different conditions, including psoriatic arthritis, prostate cancer, and diabetic kidney disease.
Different from traditional molecular diagnostic methods, such as ELISAs, the AI-based method combining proteomics and machine learning can highly specific and sensitively monitor multiple reactions by adopting targeted mass-spectrometry-based measurements of blood proteins. That's to say, researchers can detect and quantify protein biomarkers by accurately, rapidly, and cost-effectively measuring multiple proteins at the same time, which to some degree enhances the capability of detecting disease signatures in patient blood samples.
Validated Applications in Arthritis Form Differentiation
Though diagnostics combined with machine learning analytics are poised to have better outcomes, it requires validation before actual clinical applications. Research has been conducted to recognize the distinction between psoriatic arthritis and rheumatoid arthritis.
Both rheumatoid arthritis and psoriatic arthritis are autoimmune diseases in that the immune system mistakenly attacks healthy parts of the body, joints in particular, leading to swelling, stiffness, and pain. Diagnosis for rheumatoid arthritis is more straightforward by positive test results of blood markers, like autoantibodies. However, currently, there is no blood test or diagnostic product for psoriatic arthritis, and most patients receive the correct diagnosis of psoriatic arthritis after eight years, a time when the condition has progressed significantly and caused irreversible joint damage. Thus, it's highly necessary to promote early diagnostics of psoriatic arthritis.
The research has demonstrated that the strategy of combining machine learning analytics and proteomics can differentiate the form of psoriatic arthritis from rheumatoid arthritis more than 90 percent of the time.
Validated Applications in Kidney Disease Diagnosis
In addition, the proteomics-based machine learning approach has also been applied in the early diagnosis of kidney diseases as an alternative to conventional biomarkers. For instance, chronic kidney diseases caused by various factors, such as diabetic nephropathy, hypertension, and glomerulonephritis, are conditions that won't be aware until kidney functions are apparently impaired. Therefore, an early diagnosis, especially that can detect the exact cause of the condition, can efficiently improve subsequent treatment outcomes. Different from traditional biomarkers that may be unable to differentiate causes, a full-proteomic approach combined with machine learning algorithms can easily separate chronic kidney disease groups with different causes as well as separate them from healthy groups with high confidence.
Machine learning analytics show great potential in the diagnosis of different diseases, especially in the differentiation of various groups across large data sets. Proteomics data obtained by mass spectrometry combined with AI technology would be very useful when single biomarkers or even panels of biomarkers can't display satisfying results in common diagnostics.
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