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Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

Teixeira, Pedro F ; Battelino, Tadej ; Carlsson, Anneli LU orcid ; Gudbjörnsdottir, Soffia ; Hannelius, Ulf ; von Herrath, Matthias ; Knip, Mikael ; Korsgren, Olle ; Elding Larsson, Helena LU and Lindqvist, Anton , et al. (2024) In Diabetologia
Abstract

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was... (More)

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.

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publication status
epub
subject
in
Diabetologia
publisher
Springer
external identifiers
  • scopus:85185118015
  • pmid:38353727
ISSN
1432-0428
DOI
10.1007/s00125-024-06089-5
language
English
LU publication?
yes
additional info
© 2024. The Author(s).
id
9e55cd34-1264-4c7c-bb58-a38898bad5c6
date added to LUP
2024-02-17 11:39:44
date last changed
2024-04-21 06:33:04
@article{9e55cd34-1264-4c7c-bb58-a38898bad5c6,
  abstract     = {{<p>The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.</p>}},
  author       = {{Teixeira, Pedro F and Battelino, Tadej and Carlsson, Anneli and Gudbjörnsdottir, Soffia and Hannelius, Ulf and von Herrath, Matthias and Knip, Mikael and Korsgren, Olle and Elding Larsson, Helena and Lindqvist, Anton and Ludvigsson, Johnny and Lundgren, Markus and Nowak, Christoph and Pettersson, Paul and Pociot, Flemming and Sundberg, Frida and Åkesson, Karin and Lernmark, Åke and Forsander, Gun}},
  issn         = {{1432-0428}},
  language     = {{eng}},
  month        = {{02}},
  publisher    = {{Springer}},
  series       = {{Diabetologia}},
  title        = {{Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data}},
  url          = {{http://dx.doi.org/10.1007/s00125-024-06089-5}},
  doi          = {{10.1007/s00125-024-06089-5}},
  year         = {{2024}},
}