Identifying Thyroid Dysfunction Using Standard Laboratory Testings – A Systematic Review

Abstract

Thyroid dysfunction includes various thyroid-related illnesses, with subclinical hypothyroidism or hyperthyroidism at the initial stage. Blood TSH and T4 levels indicate a neutral ground between clinical conditions. Following guidelines for levothyroxine administration based on thyroid hormonal levels is beneficial for hypothyroid patients. TSH, a measurable signal, is crucial for assessing thyroid activity, with reference ranges determined by testing facilities. However, elevated TSH levels require consideration of the patient's history and lifestyle before intervention. A Machine Learning-based Thyroid Dysfunction Identification (ML-TDI) model was developed to screen individuals for medicinal intervention. Despite its prevalence and health consequences, thyroid dysfunction often goes undiagnosed. The study used standard laboratory data and machine learning algorithms to identify thyroid dysfunction, suggesting the potential for technology-driven screenings during routine medical procedures. However, assessing the benefits and risks of widespread thyroid illness evaluation requires well-conducted randomized trials.