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Carles Zafon Department of Endocrinology, Hospital Vall d'Hebron, and Diabetes and Metabolism Research Unit, Vall d'Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona and CIBERDEM (ISCIII), Barcelona

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Juan J. Díez Department of Endocrinology and Nutrition, Hospital Ramón y Cajal
Department of Medicine, University of Alcalá de Henares, Madrid

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Juan C. Galofré Department of Endocrinology and Nutrition, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
IdiSNA (Instituto de investigación en la salud de Navarra), Pamplona, Spain

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David S. Cooper Division of Endocrinology, Diabetes and Metabolism, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

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Artificial Intelligence Artificial neural networks are statistical machine learning models that emulate the processing performance of biological neurons [ 25 ]. Artificial neural network models process input data, learn from experiences, and discover

Article Type:
Review Article
DOI:
https://doi.org/10.1159/000457793
Volume/Issue:
Volume 6: Issue 2
Free access
Anita Boelen Endocrine Laboratory, Department of Laboratory Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands
Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
Amsterdam Reproduction & Development Research Institute, Amsterdam, The Netherlands

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Nitash Zwaveling-Soonawala Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
Department of Pediatric Endocrinology, Emma Children’s Hospital, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands

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Annemieke C Heijboer Endocrine Laboratory, Department of Laboratory Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands
Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
Amsterdam Reproduction & Development Research Institute, Amsterdam, The Netherlands
Endocrine Laboratory, Department of Laboratory Medicine, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

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A S Paul van Trotsenburg Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
Department of Pediatric Endocrinology, Emma Children’s Hospital, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands

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machine learning techniques in order to further improve the PPV while keeping its sensitivity. Preliminary results are promising. Recommendations Neonatal screening programs should include (total) T4 as a primary marker to allow the detection of

Article Type:
Review Article
Collections:
Hypothyroidism
DOI:
https://doi.org/10.1530/ETJ-23-0041
Volume/Issue:
Volume 12: Issue 4
Open access
Peter PA Smyth UCD School of Medicine, University College Dublin, Dublin, Ireland

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Colin D O’Dowd Ryan Institute’s Centre for Climate & Air Pollution Studies, School of Physics, University of Galway, Ireland

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from seaweed contribute to dietary iodine intake? Environmental Geochemistry and Health 2011 33 389 – 397 . ( https://doi.org/10.1007/s10653-011-9384-4 ) 15 Sherwen T Chance RJ Tinel L Ellis D Evans MJ & Carpenter LJ . A machine-learning

Article Type:
Review Article
Collections:
Environment and thyroid function
DOI:
https://doi.org/10.1530/ETJ-23-0200
Volume/Issue:
Volume 13: Issue 2
Open access
Fabyan Esberard de Lima Beltrão Lauro Wanderley University Hospital, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
University Center of João Pessoa – UNIPE, João Pessoa, PB, Brazil

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Daniele Carvalhal de Almeida Beltrão University Center of João Pessoa – UNIPE, João Pessoa, PB, Brazil
Post-Graduation Program in Cognitive Neuroscience and Behavior, Psychology Department of the Center of Human Sciences, Federal University of Paraíba, João Pessoa, Paraíba, Brazil

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Giulia Carvalhal Center for Biological and Health Sciences, Federal University of Campina Grande, Campina Grande, Paraíba, Brazil

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Fabyanna Lethicia de Lima Beltrão Post-Graduate Program in Medicine and Health, Medical School of Medicine, Federal University of Bahia, Salvador, Brazil

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Jocyel de Brito Oliveira Bioregulation Department, Health and Science Institut, Federal University of Bahia, Salvador, Bahia, Brazil

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Hatilla dos Santos Silva Bioregulation Department, Health and Science Institut, Federal University of Bahia, Salvador, Bahia, Brazil

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Helena Mariana Pitangueira Teixeira Bioregulation Department, Health and Science Institut, Federal University of Bahia, Salvador, Bahia, Brazil

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Juliana Lopes Rodrigues Laboratory of Immunopharmacology and Molecular Biology, Health Sciences Institute, Federal University of Bahia, Brazil

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Camila Alexandrina Viana de Figueiredo Laboratory of Immunopharmacology and Molecular Biology, Health Sciences Institute, Federal University of Bahia, Brazil

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Ryan dos Santos Costa Laboratory of Immunopharmacology and Molecular Biology, Health Sciences Institute, Federal University of Bahia, Brazil

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Fabio Hecht The Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

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Giciane Carvalho Vieira Post-Graduation Program in Cognitive Neuroscience and Behavior, Psychology Department of the Center of Human Sciences, Federal University of Paraíba, João Pessoa, Paraíba, Brazil

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Maria da Conceição Rodrigues Gonçalves Lauro Wanderley University Hospital, Federal University of Paraíba, João Pessoa, Paraíba, Brazil

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Antonio C. Bianco Section of Endocrinology and Metabolism, Division of the Biological Sciences, University of Chicago, Chicago, Illinois, USA

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Helton Estrela Ramos Post-Graduate Program in Medicine and Health, Medical School of Medicine, Federal University of Bahia, Salvador, Brazil
Postgraduate Program in Interactive Processes of Organs and Systems, Health & Science Institute, Federal University of Bahia, Salvador, BA, Brazil

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skeletal muscle CT radiomics and machine learning . Thoracic Cancer 2020 11 2650 – 2659 . ( https://doi.org/10.1111/1759-7714.13598 ) 30 Piotrowicz K Ryś M Perera I Gryglewska B Fedyk-Łukasik M Michel JP Wizner B Sydor W Olszanecka A

Article Type:
Research Article
Collections:
Genetics and omics in thyroidology
DOI:
https://doi.org/10.1530/ETJ-24-0068
Volume/Issue:
Volume 13: Issue 4
Open access
Georgios Kostopoulos Department of Endocrinology and Metabolism, Ippokratio General Hospital of Thessaloniki, Greece

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Grigoris Effraimidis Department of Endocrinology and Metabolic Diseases, Larissa University Hospital, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece

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). Furthermore, C 2 HEST predicts 1-year AF and may be applicable to patients with hyperthyroidism. Before implementation in clinical practice, external validation of C 2 HEST model in patients with hyperthyroidism is of cardinal importance. Of note, a machine-learning

Article Type:
Review Article
Collections:
Hyperthyroidism and thyrotoxicosis
DOI:
https://doi.org/10.1530/ETJ-23-0254
Volume/Issue:
Volume 13: Issue 2
Open access