Digital Medicine in Thyroidology: A New Era of Managing Thyroid Disease

Abstract

Digital medicine has the capacity to affect all aspects of medicine, including disease prediction, prevention, diagnosis, treatment, and post-treatment management. In the field of thyroidology, researchers are also investigating potential applications of digital technology for the thyroid disease. Recent studies using artificial intelligence (AI)/machine learning (ML) have reported reasonable performance for the classification of thyroid nodules based on ultrasonographic (US) images. AI/ML-based methods have also shown good diagnostic accuracy for distinguishing between benign and malignant thyroid lesions based on cytopathologic findings. Assistance from AI/ML methods could overcome the limitations of conventional thyroid US and fine-needle aspiration cytology. A web-based database has been developed for thyroid cancer care. In addition to its role as a nationwide registry of thyroid cancer, it is expected to serve as a clinical platform to facilitate better thyroid cancer care and as a research platform providing comprehensive disease-specific big data. Evidence has been found that biosignal monitoring with wearable devices may predict thyroid dysfunction. This real-world thyroid function monitoring could aid in the management and early detection of thyroid dysfunction. In the thyroidology field, research involving the range of digital medicine technologies and their clinical applications is expected to be even more active in the future.

Diagnostic Performance of Kwak, EU, ACR, and Korean TIRADS as Well as ATA Guidelines for the Ultrasound Risk Stratification of Non-Autonomously Functioning Thyroid Nodules in a Region with Long History of Iodine Deficiency: A German Multicenter Trial

“…In addition, promising data already exist regarding the use of artificial intelligence (AI) to identify ultrasound patterns. This technique could significantly reduce interobserver variability and account for regional differences such as site-typical normal findings via variable databases [52]. Another important pillar in the evaluation of TNs is related to the aforementioned topics: the establishment of (automated) structured reporting (SR).…”

Prediction of postpartum pelvic floor dysfunction with a nomogram model based on big data collected during pregnancy

“…Artificial intelligence and big data research have undergone a boom in the medical field in recent years, and their high accuracy and efficiency have shown tremendous advantages in medical diagnosis. Currently, the use of artificial intelligence and big data in the diagnosis of cancers such as breast cancer, lung cancer, and thyroid cancer has gradually been recognized, and its accuracy in identifying benign and malignant nodules in lung cancer screening has reached 74.3% (8)(9)(10). A nomogram PFD prediction model based on the large amount of accumulated prenatal check-up data was established in this study, with the aim of identifying women at high risk for postpartum PFD through the model and formulating personalized prevention strategies as early as possible to effectively reduce the incidence of PFD.…”

Associations of Thyroid Hormones and Resting Heart Rate in Patients Referred to Coronary Angiography

“…By contrast, there is also evidence suggesting some beneficial cardiovascular effects of thyroid hormone replacement in NTIS such as in patients with myocardial infarction or heart failure warranting further RCTs to test these hypotheses [32][33][34]. Apart from this, our findings may also pave the way for further studies to evaluate whether monitoring RHR may be a useful tool for guiding treatment decisions regarding antithyroid drug titration in patients with hyperthyroidism and for the potential detection of a (re-) emerging hyperthyroidism [35,36].…”