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