Artificial intelligence evaluation of electrocardiographic characteristics and interval changes in transgender patients on gender-affirming hormone therapy.

Aims: Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on electrocardiogram (ECG) remain unknown.

Precision fetal cardiology detects cyanotic congenital heart disease using maternal saliva metabolome and artificial intelligence.

Prenatal sonographic diagnosis of congenital heart disease (CHD) can lead to improved morbidity and mortality. However, the diagnostic accuracy of ultrasound, the sole prenatal screening tool, remains limited. Failed prenatal or early newborn detection of cyanotic CHD (CCHD) can have disastrous consequences.

Deep Neural Network Analysis of the 12-Lead Electrocardiogram Distinguishes Patients With Congenital Long QT Syndrome From Patients With Acquired QT Prolongation.

Objective: To test whether an artificial intelligence (AI) deep neural network (DNN)-derived analysis of the 12-lead electrocardiogram (ECG) can distinguish patients with long QT syndrome (LQTS) from those with acquired QT prolongation.

Methods: The study cohort included all patients with genetically confirmed LQTS evaluated in the Windland Smith Rice Genetic Heart Rhythm Clinic and controls from Mayo Clinic's ECG data vault comprising more than 2.5 million patients.