Diagnosing acute bilirubin encephalopathy in neonates using MRI-based deep learning model.

ABSTRACT:

Background

Neonatal acute bilirubin encephalopathy (ABE) severely endangers the neonatal health. However, early clinical symptoms of ABE are nonspecific, often leading to missed diagnoses. The current study endeavors to establish a computer-assisted integrated model for clinical assessment and diagnosis of ABE.

Methods

Diagnostic data from the ABE group and the hyperbilirubinemia without concurrent ABE (non-ABE) group were retrospectively analyzed. Patients were divided into a pre-training cohort, a training cohort, and two test cohorts. The training cohort and test cohort 1 were used to train and test a deep learning (DL) model integrating multimodal, self-supervised, and multi-instance learning. Test cohort 2 was used to compare the DL model with the radiologists.

Resul

SUBMITTER: Huang K 

PROVIDER: S-EPMC12538867 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature