Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism.

ABSTRACT:

Aims

Although overt hyperthyroidism adversely affects a patient's prognosis, thyroid function tests (TFTs) are not routinely conducted. Furthermore, vague symptoms of hyperthyroidism often lead to hyperthyroidism being overlooked. An electrocardiogram (ECG) is a commonly used screening test, and the association between thyroid function and ECG is well known. However, it is difficult for clinicians to detect hyperthyroidism through subtle ECG changes. For early detection of hyperthyroidism, we aimed to develop and validate an electrocardiographic biomarker based on a deep learning model (DLM) for detecting hyperthyroidism.

Methods and results

This multicentre retrospective cohort study included patients who underwent ECG and TFTs within 24 h. For model development and interna

SUBMITTER: Choi B 

PROVIDER: S-EPMC9707932 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature