AIM: To study the role of iodine, selenium and zinc in the pathogenesis of iodine deficiency and autoimmune thyroid diseases and scientifically substantiate the choice of security biomarkers and analytical methods for determination.
BACKGROUND: Artificial Intelligence provides numerous applications in the healthcare sector. The main aim of this study is to evaluate the extent of the current application of artificial intelligence in thyroid diagnostics.
Thyroid syndrome, a complex endocrine disorder, involves the dysregulation of the thyroid gland, impacting vital physiological functions. Common causes include autoimmune disorders, iodine deficiency, and genetic predispositions. The effects of thyro...
Technology and health care : official journal of the European Society for Engineering and Medicine
39058467
BACKGROUND: Early pregnancy thyroid function assessment in mothers is covered. The benefits of using load-specific reference ranges are well-established.
Thyroid disease classification plays a crucial role in early diagnosis and effective treatment of thyroid disorders. Machine learning (ML) techniques have demonstrated remarkable potential in this domain, offering accurate and efficient diagnostic to...
BMC medical informatics and decision making
39614307
Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significantly impact metabolism and overall health. Hypothyroidism can cause a slowdown in bodily processes, ...
OBJECTIVE: This study aims to characterize and analyze the expression of representative biomarkers like lymphocytes and immune subsets in children with thyroid disorders. It also intends to develop and evaluate a machine learning model to predict if ...
Exposure to organochlorine pesticides (OCPs) poses significant health risks, including cancer, endocrine dysregulation, neurological disorders, and reproductive disruption. This study investigates the association between OCP exposure and thyroid dist...
BACKGROUND: Unlike one-snap data collection methods that only identify high-risk patients, machine learning models using time-series data can predict adverse events and aid in the timely management of cancer.