Western Sydney University team develops AI tool for type 1 diabetes
A research team led by Western Sydney University in Australia has developed an AI-powered tool that could evaluate the development risk of type 1 diabetes (T1D) and forecast treatment responses.
The tool utilises microRNAs, small RNA molecules from blood, to create a Dynamic Risk Score (DRS4C) that distinguishes those with or without T1D.
The DRS4C was developed after analysing molecular data from 5,983 study samples across Australia, Canada, China, Denmark, Hong Kong Special Administrative Region (SAR), India, New Zealand, and the US.
With AI utilisation, the risk score was further validated in 662 subjects, predicting which individuals would remain insulin-free an hour post-therapy.
The microRNA markers forecasted early responses to treatments such as islet transplantation and the drug imatinib.
This new risk score captures the changing risk of T1D and can differentiate between type 1 and type 2 diabetes.
The university's School of Medicine and Translational Health Research Institute professor Anand Hardikar highlighted the significance of this advancement, given that current T1D testing methods have not seen major changes for years.
Hardikar said: 'T1D risk prediction is timely, with therapies that can delay T1D progression becoming recognised and available. Since early-onset T1D before the age of ten years is particularly aggressive and linked to up to 16 years of reduced life expectancy, accurately predicting progression gives doctors a powerful tool to intervene sooner.'
Lead researcher Dr Mugdha Joglekar from the School of Medicine and Translational Health Research Institute distinguished between genetic and dynamic risk markers, noting that the genetic testing provided a static risk view.
The study involved 79 researchers from 33 institutions across seven nations.
Funding for this research was provided by entities such as Breakthrough T1D (formerly JDRF Australia), the Australian Research Council, and the National Health and Medical Research Council, with additional backing from Western Sydney University and the Danish Diabetes and Endocrine Academy.
"Western Sydney University team develops AI tool for type 1 diabetes" was originally created and published by Medical Device Network, a GlobalData owned brand.
The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site.
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Western Sydney University team develops AI tool for type 1 diabetes
A research team led by Western Sydney University in Australia has developed an AI-powered tool that could evaluate the development risk of type 1 diabetes (T1D) and forecast treatment responses. The tool utilises microRNAs, small RNA molecules from blood, to create a Dynamic Risk Score (DRS4C) that distinguishes those with or without T1D. The DRS4C was developed after analysing molecular data from 5,983 study samples across Australia, Canada, China, Denmark, Hong Kong Special Administrative Region (SAR), India, New Zealand, and the US. With AI utilisation, the risk score was further validated in 662 subjects, predicting which individuals would remain insulin-free an hour post-therapy. The microRNA markers forecasted early responses to treatments such as islet transplantation and the drug imatinib. This new risk score captures the changing risk of T1D and can differentiate between type 1 and type 2 diabetes. The university's School of Medicine and Translational Health Research Institute professor Anand Hardikar highlighted the significance of this advancement, given that current T1D testing methods have not seen major changes for years. Hardikar said: 'T1D risk prediction is timely, with therapies that can delay T1D progression becoming recognised and available. Since early-onset T1D before the age of ten years is particularly aggressive and linked to up to 16 years of reduced life expectancy, accurately predicting progression gives doctors a powerful tool to intervene sooner.' Lead researcher Dr Mugdha Joglekar from the School of Medicine and Translational Health Research Institute distinguished between genetic and dynamic risk markers, noting that the genetic testing provided a static risk view. The study involved 79 researchers from 33 institutions across seven nations. Funding for this research was provided by entities such as Breakthrough T1D (formerly JDRF Australia), the Australian Research Council, and the National Health and Medical Research Council, with additional backing from Western Sydney University and the Danish Diabetes and Endocrine Academy. "Western Sydney University team develops AI tool for type 1 diabetes" was originally created and published by Medical Device Network, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site. Error al recuperar los datos Inicia sesión para acceder a tu cartera de valores Error al recuperar los datos Error al recuperar los datos Error al recuperar los datos Error al recuperar los datos
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