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AI role in diagnostics

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Technology Singapore CU- Artificial Intelligence (AI) is rapidly transforming the world as we know it, with advances in voice over technology, streaming, customer support, security and health care. The role of AI is likely to advance medicine further with researchers from the Nayang Technological University (NTU) who have found a new AI technique to diagnose mental health issues with the use of biomarkers.

Biomarkers are biological substances often used in medical diagnostics for cancer, heart disease, asthma, rheumatoid arthritis and more. Biomarkers often help in detecting irregular patterns but the diagnosis of mental health issues with biomarkers has often been a challenge.

Mental health issues are often diagnosed based on the symptoms described by the patient. Researchers hope to use the new technology to analyze huge datasets effectively. The technology could also help forecast mental conditions in individuals who are prone to more severe issues, where personalized tailor-made solutions can be recommended.

Researchers are currently looking into the possibility of applying the technology on specific mental conditions such as schizophrenia, which is a condition that often causes a person to fail to distinguish between realities and sometimes take on multiple personalities. The researchers at the NTU have teamed up for the New Zealand-Singapore Data Science Research Programme, where the Institute of Mental Health in Singapore and Auckland University of Technology have also joined hands to forecast schizophrenia in youth.

The researchers are looking into the analysis of expression and metabolic profiles in blood collections of 600 youth which they hope to follow with clinical and behavioral datasets of the participants.

The research team also hopes to implement the new technology in dementia which often results in memory loss confusion and many auditory and visual problems. The researchers hope to use the technology to predict and delay progression of dementia and are currently working with 1,500 of early-stage dementia participants of all backgrounds for a 5-year period to better analyze the sequence of events of the brain in the early stages of dementia.

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