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Kristine Z Swan Department of ORL, Head- and Neck Surgery, Aarhus University Hospital, Aarhus, Denmark

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Johnson Thomas Department of Endocrinology, Mercy Hospital, Springfield, Missouri, USA

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Viveque E Nielsen Department of ORL, Head- and Neck Surgery, Odense University Hospital, Odense, Denmark

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Marie Louise Jespersen Department of Pathology, Aarhus University Hospital, Aarhus, Denmark

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Steen J Bonnema Department of Endocrinology, Odense University Hospital, Odense, Denmark

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reliable, explainable, less subjective, and noninvasive technique to address this problem is desirable. AIBx is an artificial intelligence (AI) model which might overcome these challenges ( 10 ). The AIBx algorithm retrieves images from an image library

Article Type:
Research Article
Collections:
Goiter and thyroid nodules
DOI:
https://doi.org/10.1530/ETJ-21-0129
Volume/Issue:
Volume 11: Issue 2
Open access
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
Lei Xu Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
Xi’an Hospital of Traditional Chinese Medicine, Xi’an, China

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Junling Gao Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China

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Quan Wang Laboratory of Surgical Oncology, Peking University People’s Hospital, Peking University, Beijing, China

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Jichao Yin Xi’an Hospital of Traditional Chinese Medicine, Xi’an, China

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Pengfei Yu Xijing Hospital, Fourth Military Medical University, Xi’an, China

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Bin Bai Xijing Hospital, Fourth Military Medical University, Xi’an, China

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Ruixia Pei Xi’an Hospital of Traditional Chinese Medicine, Xi’an, China

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Dingzhang Chen Xijing Hospital, Fourth Military Medical University, Xi’an, China

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Guochun Yang Xi’an Hospital of Traditional Chinese Medicine, Xi’an, China

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Shiqi Wang Xijing Hospital, Fourth Military Medical University, Xi’an, China

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Mingxi Wan Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China

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). There are some limitations of the present study. First, various artificial intelligence models were combined in the meta-analysis, and this may have introduced statistical heterogeneity. To decrease this kind of heterogeneity, classic machine learning

Article Type:
Systematic Review
DOI:
https://doi.org/10.1159/000504390
Volume/Issue:
Volume 9: Issue 4
Free access
Haiyang Zhang Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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Shuo Wu Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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Shuyu Hu Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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Xianqun Fan Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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Xuefei Song Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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Tienan Feng Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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Huifang Zhou Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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heterogeneity profiling through high-dimensional quantitative data extracted from radiological images ( 27 , 36 ). Furthermore, the application of advanced modeling techniques like machine learning and artificial intelligence ( 37 ) empowers the construction of

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Systematic Review
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Hyperthyroidism and thyrotoxicosis
DOI:
https://doi.org/10.1530/ETJ-24-0122
Volume/Issue:
Volume 13: Issue 4
Open access
Roberto Negro Division of Endocrinology, “V. Fazzi” Hospital, Lecce, Italy

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Pierpaolo Trimboli Clinic for Nuclear Medicine and Competence Center for Thyroid Diseases, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland

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investigation. In addition to the traditional ultrasound evaluation, the usage of instruments such as molecular markers involved in cell proliferation, spectral analysis to categorize nodule composition, or artificial intelligence to predict TA efficacy, may in

Article Type:
Editorial
DOI:
https://doi.org/10.1159/000509310
Volume/Issue:
Volume 9: Issue 4
Free access
Tommaso Piticchio Endocrinology Section, Department of Clinical and Experimental Medicine, Garibaldi Nesima Hospital, University of Catania, Catania, Italy
Servizio di Endocrinologia e Diabetologia, Ospedale Regionale di Lugano, Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland

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Gilles Russ Department of Thyroid and Endocrine Tumor Diseases, La Pitie-Salpetriere Hospital, 83 Bd de l’Hopital, Paris, France

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Maija Radzina Riga Stradins University, Radiology Research Laboratory, Riga, Latvia
University of Latvia, Faculty of Medicine, Riga, Latvia

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Francesco Frasca Endocrinology Section, Department of Clinical and Experimental Medicine, Garibaldi Nesima Hospital, University of Catania, Catania, Italy

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Cosimo Durante Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy

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Pierpaolo Trimboli Servizio di Endocrinologia e Diabetologia, Ospedale Regionale di Lugano, Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
Facoltà di Scienze Biomediche, Università della Svizzera Italiana (USI), Lugano, Switzerland

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classification aspects are also essential for the implementation of artificial intelligence algorithms in routine thyroid ultrasound ( 27 ). Strengths and potential limitations of the paper should be addressed. Occasionally, the research aim was somewhat

Article Type:
Review Article
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Goiter and thyroid nodules
DOI:
https://doi.org/10.1530/ETJ-23-0242
Volume/Issue:
Volume 13: Issue 2
Open access
J L Reverter Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain

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L Ferrer-Estopiñan Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain

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F Vázquez Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain

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S Ballesta Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain

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S Batule Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain

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A Perez-Montes de Oca Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain

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C Puig-Jové Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain

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M Puig-Domingo Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain

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designed to facilitate the detection, visualization, and characterization of thyroid nodule features in sonographic images using artificial intelligence and computational vision recognition and quantification algorithms. The program evaluates nodule shape

Article Type:
Research Article
Collections:
Goiter and thyroid nodules
DOI:
https://doi.org/10.1530/ETJ-21-0023
Volume/Issue:
Volume 11: Issue 1
Open access
Heleen I Jansen Department of Laboratory Medicine, Endocrine Laboratory, Amsterdam UMC location Vrije Universiteit Amsterdam, Boelelaan, Amsterdam, The Netherlands
Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Laboratory Medicine, Endocrine Laboratory, Amsterdam UMC location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands

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Marije van Haeringen Department of Laboratory Medicine, Endocrine Laboratory, Amsterdam UMC location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands
Department of Computer Science, Vrije Universiteit, Boelelaan, Amsterdam, The Netherlands

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Marelle J Bouva Reference Laboratory Neonatal Screening, Center for Health protection, National Institute for Public Health and the Environment, Bilthoven, The Netherlands

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Wendy P J den Elzen Department of Laboratory Medicine, Laboratory Specialized Diagnostics & Research, Amsterdam UMC, University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands
Amsterdam Public Health, Amsterdam, The Netherlands

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Eveline Bruinstroop Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Endocrinology and Metabolism, Amsterdam UMC location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands

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Catharina P B van der Ploeg TNO - Child Health, Sylviusweg, Leiden, The Netherlands

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

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

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

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Annet M Bosch Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Pediatrics, Division of Metabolic Disorders, Emma Children’s Hospital, Amsterdam UMC, University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands

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Robert de Jonge Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Laboratory Medicine, Amsterdam UMC, Vrije Universiteit, Boelelaan, Amsterdam, The Netherlands
Department of Laboratory Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands

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Mark Hoogendoorn Department of Computer Science, Vrije Universiteit, Boelelaan, Amsterdam, The Netherlands

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Anita Boelen Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Laboratory Medicine, Endocrine Laboratory, Amsterdam UMC location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands
Amsterdam Reproduction & Development Research Institute, Amsterdam, The Netherlands

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-sampling technique . Journal of Artificial Intelligence Research 2002 16 321 – 357 . ( https://doi.org/10.1613/jair.953 ) 20 Stekhoven DJ & MissForest BP . Non-parametric missing value imputation for mixed-type data . Bioinformatics 2011 28 112 – 118

Article Type:
Research Article
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Pediatric thyroid diseases
DOI:
https://doi.org/10.1530/ETJ-23-0141
Volume/Issue:
Volume 12: Issue 6
Open access
Tomohiro Kikuchi Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan
Department of Radiology, Jichi Medical University, School of Medicine, Shimotsuke, Tochigi, Japan

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Shouhei Hanaoka Department of Radiology, The University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan

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Takahiro Nakao Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan

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Yukihiro Nomura Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan
Center for Frontier Medical Engineering, Chiba University, Yayoicho, Inage–ku, Chiba, Japan

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Takeharu Yoshikawa Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan

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Ashraful Alam Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan

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Harushi Mori Department of Radiology, Jichi Medical University, School of Medicine, Shimotsuke, Tochigi, Japan

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Naoto Hayashi Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan

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. Nuclear Medicine and Molecular Imaging 2020 54 241 – 248 . ( https://doi.org/10.1007/s13139-020-00659-2 ) 5 Hirata K Sugimori H Fujima N Toyonaga T Kudo K . Artificial intelligence for nuclear medicine in oncology . Annals of Nuclear Medicine

Article Type:
Research Article
Collections:
Goiter and thyroid nodules
DOI:
https://doi.org/10.1530/ETJ-22-0165
Volume/Issue:
Volume 12: Issue 1
Open access