Enhancing TSH-based congenital hypothyroidism screening using machine learning and resampling algorithms.

ABSTRACT:

Purpose

Congenital hypothyroidism (CH) is a common cause of severe intellectual disability, affecting approximately 1 in 2,000 newborns globally. Treatable with early intervention, congenital hypothyroidism has long been a target of newborn screening programs. Current thyroid stimulating hormone (TSH) based programs suffer from low positive predictive value, resulting in unnecessary diagnostic investigations. Congenital hypothyroidism screening has proven challenging for machine learning previously due to massive class imbalance and having a single well known predictor, preventing acceptable screening sensitivity. This study represents the most comprehensive evaluation of machine learning for congenital hypothyroidism screening to date.

Methods

Analyzing data from 616,910 in

SUBMITTER: De Furia A 

PROVIDER: S-EPMC12751215 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature