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Urszula Smyczyńska Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Krakow, Poland

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Joanna Smyczyńska Department of Endocrinology and Metabolic Diseases, Polish Mother’s Memorial Hospital – Research Institute, Lodz, Poland

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Maciej Hilczer Department of Endocrinology and Metabolic Diseases, Polish Mother’s Memorial Hospital – Research Institute, Lodz, Poland
Department of Paediatric Endocrinology, Medical University of Lodz, Lodz, Poland

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Renata Stawerska Department of Endocrinology and Metabolic Diseases, Polish Mother’s Memorial Hospital – Research Institute, Lodz, Poland

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Ryszard Tadeusiewicz Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Krakow, Poland

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Andrzej Lewiński Department of Endocrinology and Metabolic Diseases, Polish Mother’s Memorial Hospital – Research Institute, Lodz, Poland
Department of Endocrinology and Metabolic Diseases, Medical University of Lodz, Lodz, Poland

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recently presented for the first time models of prediction of FH of GH-deficient children with the use of artificial neural networks (ANN) ( 15 ). Neural networks are very complex computational systems, designed to some extent to resemble neuronal

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-17-0277
Volume/Issue:
Volume 7: Issue 1
Open access
Pablo Abellán-Galiana Department of Endocrinology, Hospital General Universitari de Castelló, Castellón, Spain
Department of Medicine, Universidad Cardenal Herrera-CEU, CEU Universities, Castellón, Spain

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Carmen Fajardo-Montañana Department of Endocrinology, Hospital Universitario de la Ribera, Alzira, Spain

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Pedro Riesgo-Suárez Department of Neurosurgery, Hospital Universitario de la Ribera, Alzira, Spain

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Marcelino Pérez-Bermejo Department of Nursing, Universidad Católica de Valencia, Valencia, Spain

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Celia Ríos-Pérez Centro de Salud Tavernes de la Valldigna, Hospital Comarcal Francesc de Borja, Gandía, Spain

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José Gómez-Vela Department of Endocrinology, Hospital Universitario de la Ribera, Alzira, Spain

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and entered in an MS Excel 2016 spreadsheet (Microsoft Office 365) for subsequent analysis using the IBM SPSS version 23.0 statistical package. Descriptive, bivariate and multivariate analyses (artificial neural networks (ANNs)) were performed, with

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-19-0297
Volume/Issue:
Volume 8: Issue 9
Open access
Nidan Qiao Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
Neuroendocrine Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

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. ( https://doi.org/10.1007/11866763_61 ) 7 Kitajima M Hirai T Katsuragawa S Okuda T Fukuoka H Sasao A Akter M Awai K Nakayama Y Ikeda R , et al . Differentiation of common large sellar-suprasellar masses effect of artificial neural

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-19-0156
Volume/Issue:
Volume 8: Issue 7
Open access
Xiaohui Weng School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China
Weihai Institute for Bionics, Jilin University, Weihai, China

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Gehong Li School of Mathematics, Jilin University, Changchun, China

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Ziwei Liu Department of endocrinology, Jinshan Branch of Shanghai Sixth People's Hospital, Shanghai, China

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Rui Liu Department of VIP Unit, China-Japan Union Hospital of Jilin University, Changchun, China

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Zhaoyang Liu Digital Intelligent Cockpit Department, Intelligent Connected Vehicle Development Institute, China FAW Group Co LTD, Changchun, China

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Songyang Wang Digital Intelligent Cockpit Department, Intelligent Connected Vehicle Development Institute, China FAW Group Co LTD, Changchun, China

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Shishun Zhao School of Mathematics, Jilin University, Changchun, China

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Xiaotong Ma School of Mathematics, Jilin University, Changchun, China

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Zhiyong Chang Weihai Institute for Bionics, Jilin University, Weihai, China
College of Biological and Agricultural Engineering, Jilin University, Changchun, China
Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, China

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). These technologies integrate artificial neural networks and improve existing clinical disease detection methods ( 5 ). Studies have shown that the concentration of acetone ( 6 , 7 ) and some VOCs ( 8 ) in diabetic patients is abnormal. The relationship

Article Type:
Research Article
Collections:
Metabolic Syndrome and Diabetes
DOI:
https://doi.org/10.1530/EC-22-0437
Volume/Issue:
Volume 12: Issue 3
Open access
Qiuyu Huang Department of Cardiovascular Surgery, Union Hospital, Fujian Medical University, Fuzhou, Fujian Province, China
Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, Fujian Province, China

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Hanshen Chen Department of Anesthesiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China

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Fan Xu Department of Cardiovascular Surgery, Union Hospital, Fujian Medical University, Fuzhou, Fujian Province, China
Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, Fujian Province, China

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Chao Liu Department of Cardiothoracic Surgery, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, Jiangsu Province, China

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Yafeng Wang Department of Cardiology, The People’s Hospital of Xishuangbanna Dai Autonomous Prefecture, Jinghong, Yunnan Province, China

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Weifeng Tang Department of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Nanjing University Medical School, Jiangsu Province, China

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Liangwan Chen Department of Cardiovascular Surgery, Union Hospital, Fujian Medical University, Fuzhou, Fujian Province, China
Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, Fujian Province, China

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included by artificial neural networks database. Thirdly, for temporary limitation of T2DM sample size and type, we couldn’t perform genotype-based mRNA expression analysis. However, further investigations with detailed gene–environmental factors and

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-21-0261
Volume/Issue:
Volume 10: Issue 11
Open access