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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; Gudbjörnsdottir, Soffia; Hannelius, Ulf; von Herrath, Matthias; Knip, Mikael; Korsgren, Olle; Elding Larsson, Helena; Lindqvist, Anton; Ludvigsson, Johnny; Lundgren, Markus; Nowak, Christoph; Pettersson, Paul; Pociot, Flemming; Sundberg, Frida; Åkesson, Karin; Lernmark, Åke; Forsander, Gun.
Afiliação
  • Teixeira PF; Diamyd Medical AB, Stockholm, Sweden.
  • Battelino T; University Medical Center Ljubljana, University of Ljubljana, Ljubljana, Slovenia.
  • Carlsson A; Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
  • Gudbjörnsdottir S; Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden.
  • Hannelius U; Swedish National Diabetes Register, Centre of Registers, Gothenburg, Sweden.
  • von Herrath M; Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
  • Knip M; Diamyd Medical AB, Stockholm, Sweden.
  • Korsgren O; Global Chief Medical Office, Novo Nordisk, A/S, Søborg, Denmark.
  • Elding Larsson H; Diabetes Research Institute, University of Miami, Miami, FL, USA.
  • Lindqvist A; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
  • Ludvigsson J; Center for Child Health Research, Tampere University Hospital, Tampere, Finland.
  • Lundgren M; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
  • Nowak C; Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
  • Pettersson P; Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital, Malmö, Sweden.
  • Pociot F; Department of Pediatrics, Skåne University Hospital, Malmö, Sweden.
  • Sundberg F; Diamyd Medical AB, Stockholm, Sweden.
  • Åkesson K; Crown Princess Victoria Children's Hospital and Division of Pediatrics, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
  • Lernmark Å; Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden.
  • Forsander G; Department of Paediatrics, Kristianstad Hospital, Kristianstad, Sweden.
Diabetologia ; 67(6): 985-994, 2024 Jun.
Article em En | MEDLINE | ID: mdl-38353727
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Programas de Rastreamento / Diabetes Mellitus Tipo 1 Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Humans Idioma: En Revista: Diabetologia Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Suécia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Programas de Rastreamento / Diabetes Mellitus Tipo 1 Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Humans Idioma: En Revista: Diabetologia Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Suécia