open access publication

Article, 2024

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

Diabetologia, ISSN 0012-186X, 1432-0428, Volume 67, 6, Pages 985-994, 10.1007/s00125-024-06089-5

Contributors

Teixeira, Pedro F [1] Battelino, Tadej 0000-0002-0273-4732 [2] [3] Carlsson, Anneli K [4] Gudbjörnsdottir, Soffia [5] [6] Hannelius, Ulf 0000-0003-1562-437X [1] Von Herrath, Matthias Georg [7] [8] Knip, Mikael J 0000-0003-0474-0033 [9] [10] Korsgren, Olle 0000-0002-8524-9547 [6] [11] Elding Larsson, Helena 0000-0003-3306-1742 [4] Lindqvist, Anton [1] Ludvigsson, Johnny 0000-0003-1695-5234 [12] Lundgren, Markus 0000-0001-6394-7689 [13] [14] Nowak, Christoph 0000-0001-8435-3978 [1] Pettersson, Paul 0000-0003-4040-3480 [15] [16] Pociot, Flemming Michael 0000-0003-3274-5448 [17] [18] Sundberg, Frida [6] [19] Åkesson, Karin [12] [20] Lernmark, Å Ke 0000-0003-1735-0499 (Corresponding author) [4] Forsander, Gun 0000-0002-0266-9651 (Corresponding author) [6] [19]

Affiliations

  1. [1] Mertiva (Sweden)
    2. [NORA names: Sweden; Europe, EU; Nordic; OECD];
  2. [2] Ljubljana University Medical Centre
    3. [NORA names: Slovenia; Europe, EU; OECD];
  3. [3] University of Ljubljana
    4. [NORA names: Slovenia; Europe, EU; OECD];
  4. [4] Skåne University Hospital
    5. [NORA names: Sweden; Europe, EU; Nordic; OECD];
  5. [5] Swedish National Diabetes Register, Centre of Registers, Gothenburg, Sweden
    6. [NORA names: Miscellaneous; Sweden; Europe, EU; Nordic; OECD];

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 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.Graphical Abstract

Keywords

acceptance, accurate risk prediction, analysis, analysis of longitudinal data, approaches to disease prevention, article, artificial intelligence, assets, at-risk individuals, authors, benefits, clinical evaluation, clinicians, cohort study, community, consortium, cost-effective, data, data repositories, design, designing screening programmes, detection of individuals, diabetes, disease, disease prevention, disease risk, early detection, early detection of individuals, early screening, feasibility, follow-up plan, healthcare, healthcare providers, high risk, identification, implementation, implementation of screening, increased risk, increased risk of disease, individuals, industry, initiation, intelligence, issues, levels, longitudinal data, monitoring plan, observational cohort study, operational feasibility, operational implementation, payers, planning, population, population level, potential, potential use, precision, prediction, prevention, preventive therapy, programme, providers, regulatory authorities, repository, research, research community, risk, risk of disease, risk prediction, screening, screening programme, stratification, study, test, test variables, therapy, type, type 1 diabetes, type 1 diabetes community, use, variables

Funders

  • VINNOVA

Data Provider: Digital Science

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