Hashimoto Thyroiditis with Hypothyroidism Based on Clinical and Paraclinical Data in Female Patients

 

Hashimoto’s thyroiditis is the most common autoimmune disorder and the leading cause of hypothyroidism in iodine-sufficient areas. [1] Untreated hypothyroidism is associated with an increased risk of developing cardiovascular disease, distal polyneuropathy, depression, and myxedema. In recent years, a concept emerged, that thyroid autoimmunity could be associated with low-grade chronic inflammation, independent of thyroid function. It is, therefore, essential to diagnose Hashimoto thyroiditis as early as possible and to test for thyroid function.

Based on these observations, we will try to integrate multiple clinical and paraclinical data, in order to establish an early diagnosis and to elaborate risk prediction of Hashimoto thyroiditis and diverse classification processes, using various machine learning algorithms.

In the last years, multiple studies have tried to identify thyroid disorders early in the course of the disease, based upon the use of hormonal parameters and personal data of the patients, such as gender and age. Some of these published studies use machine learning classification and prediction models, while other studies deal with deep neural network models. The authors, Izdihar and Bozkus, [2] have developed a machine-learning tool for the diagnosis of thyroid diseases, that has the ability to make an intelligent forecast of thyroid gland diseases. Their work showed an overall accuracy of 100% for training and 98.7% for testing. They used decision tree model and support vector machines to help the doctor to label a new case using different parameters.

In our future study, about machine learning model comparison using data about Hashimoto thyroiditis, we will try to find clinical parameters, that demonstrate a significant positive correlation with Hashimoto thyroiditis. For example, age, body mass index, familial history of autoimmune thyroid disease, history of breast cancer, surgically induced menopause, diabetes mellitus type 2, and polycystic ovary syndrome.

Concerning the paraclinical parameters, we will try to see if thyroid autoantibodies, dyslipidemia, uric acid levels, fasting hyperglycemia, and abnormal liver function tests, in non-alcoholic fatty liver disease, could be associated with Hashimoto thyroiditis with hypothyroidism.

 

References

[1] Caturegli, P., De Remigis, A., & Rose, N. R. (2014). Hashimoto thyroiditis: clinical and diagnostic criteria. Autoimmunity reviews, 13(4-5), 391-397.

[2] Al-muwaffaq, I., & Bozkus, Z. (2016). MLTDD: use of machine learning techniques for diagnosis of thyroid gland disorder. Comput Sci Inf Technol, 67-3.