Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE).

Abstract

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy (HIE) using MRI and basic clinical data. Materials and Methods In this study, MRI data of term neonates with encephalopathy in the High Dose Erythropoietin for Asphyxia (HEAL) trial (ClinicalTrials.gov: NCT02811263), who were enrolled from 17 institutions between January 25th, 2017 and October ninth, 2019, were retrospectively analyzed. The harmonized MRI protocol included T1-weighted, T2-weighted, and diffusion tensor imaging. Deep learning classifiers were trained to predict the primary outcome of the HEAL trial (death or any neurodevelopmental impairment [NDI] at 2 years) using multisequence MRI and basic clinical variables, including sex and gestational age at birth. Model performance was evaluated on a test sets comprising 10% of cases from 15 institutions (in-distribution test set, n = 41) and 100% of cases from 2 institutions (out-of-distribution test set, n = 41). Model performance in predicting additional secondary outcomes, including death alone, was also assessed. Results For the 414 neonates (mean gestational age, 39 weeks ± 1.4, 232 males, 182 females), in the study cohort, 198 (48%) died or had any NDI at 2 years. The deep learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.60-0.86) and 63% accuracy on the in-distribution test set and an AUC of 0.77 (95% CI: 0.63-0.90) and 78% accuracy on the out-of-distribution test set. Performance was similar or better for predicting secondary outcomes. Conclusion Deep learning analysis of neonatal brain MRI yielded high performance for predicting 2-year neurodevelopmental outcomes. ©RSNA, 2024.