Abstract
BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard for outcome prediction after hypoxic-ischemic encephalopathy (HIE). Published scoring systems contain duplicative or conflicting elements. METHODS: Infants ≥36 weeks gestational age (GA) with moderate to severe HIE, therapeutic hypothermia treatment, and T1/T2/diffusion-weighted imaging were identified. Adverse motor outcome was defined as Bayley-III motor score <85 or alberta infant motor scale><10th centile at 12 to 24 months. mris were scored using a published scoring system. logistic regression (lr) and gradient-boosted deep learning (dl) models quantified the importance of clinical and imaging features. the cohort underwent 80 20 train test split with fivefold cross validation. feature selection eliminated low-value features. results: a total of 117 infants were identified with mean ga =" 38.6 weeks," median cord ph =" 7.01," and median 10-minute apgar =" 5." adverse motor outcome was noted in 23 of 117 (20%). putamen globus pallidus injury on t1, ga, and cord ph were the most informative features. feature selection improved model accuracy from 79% (48-feature mri model) to 85% (three-feature model). the three-feature dl model had superior performance to the best lr model (area under the receiver-operator curve 0.69 versus 0.75). conclusions: the parsimonious dl model predicted adverse hie motor outcomes with 85% accuracy using only three features (putamen globus pallidus injury on t1, ga, and cord ph) and outperformed lr.>
