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Big Data Analysis and Artificial Intelligence Study of Thyroid Disease

Submission deadline: 30 June 2022
Special Issue Editors
Kwang-Sig Lee
Anam Hospital AI Center, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
Interests: Data Analysis; Medical Informatics; Artificial Intelligence; Machine Learning
Hyuntae Park
Department of Obstetrics & Gynecology, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
Interests: Subfertility; Menopause; Menstrual Irregularity; Uterine Fibroids
Special Issue Information

Dear Colleagues,

The thyroid gland is an endocrine gland creating thyroid hormone. It is shaped like a butterfly and positioned in the front of the neck. Thyroid hormone involves the regulation of metabolism and various problems can occur in the gland. It can create either too little or too much hormone (hypothyroidism or hyperthyroidism). The former condition causes fatigue, weight gain and intolerance to cold temperature, whereas the latter leads to anxiety, weight loss and sensitivity to heat. Also, malignant cells can develop there (thyroid cancer). These disorders, thyroid disease, has been a leading cause of disease burden in the world. It has various risk factors and many of them are still unknown. Its diagnosis and prognosis are considered to be quite challenging given that its symptoms are very similar with other diseases such as depression. It is not surprising that there exists a high degree of variation among clinical experts in terms of its diagnosis and prognosis. In this context, more research is to be done on this important topic.

Recently, the words “artificial intelligence”, “machine learning”, and “deep learning” have gained strong interest around the world. For example, the Google trends for these keywords registered rapid growths from 10 to 100 from 2013 to 2018. The Merriam-Webster dictionary defines artificial intelligence as “the capability of a machine to imitate intelligent human behavior”. Machine learning (or data mining) is a branch of artificial intelligence to extract knowledge from large amounts of data. Emerging literature pays due attention to the application of machine learning on the diagnosis of the thyroid disease. However, its focus has been limited to EMR/ultrasound data on triiodothyronine, thyroxine, thyroid stimulating hormone, thyroid nodule and related medication. It can be extended to various types of data on a greater scope of predictors. In this context, this special issue is entitled as “Big Data Analysis and Artificial Intelligence Study of Thyroid Disease”. Original articles and review articles will be equally welcomed for this issue.

Assoc. Prof. Kwang-Sig Lee and Prof. Hyuntae Park

Guest Editors

Manuscript Submission Information

Manuscripts should be submitted via our online editorial system at https://imr.propub.com by registering and logging in to this website. Once you are registered, click here to start your submission. Manuscripts can be submitted now or up until the deadline. All papers will go through peer-review process. Accepted papers will be published in the journal (as soon as accepted) and meanwhile listed together on the special issue website. Research articles, reviews as well as short communications are preferred. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office to announce on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts will be thoroughly refereed through a double-blind peer-review process. Please visit the Instruction for Authors page before submitting a manuscript. The Article Processing Charge (APC) in this open access journal is 2500 USD. Submitted manuscripts should be well formatted in good English.

Published Paper (1 Paper)
Open Access Review
Machine learning on thyroid disease: a review
Kwang-Sig Lee, Hyuntae Park
Front. Biosci. (Landmark Ed) 2022, 27(3), 101; https://doi.org/10.31083/j.fbl2703101
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