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

Abstract

Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy 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 and Encephalopathy (HEAL) trial (ClinicalTrials.gov: NCT02811263), who were enrolled from 17 institutions between January 25, 2017, and October 9, 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 at 2 years) using multisequence MRI and basic clinical variables, including sex and gestational age at birth. Model performance was evaluated on test sets comprising 10% of cases from 15 institutions (in-distribution test set, n = 41) and 10% of cases from two 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 [SD]; 232 male, 182 female), in the study cohort, 198 (48%) died or had any neurodevelopmental impairment 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 in the in-distribution test set and an AUC of 0.77 (95% CI: 0.63, 0.90) and 78% accuracy in 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. Keywords: Convolutional Neural Network (CNN), Prognosis, Pediatrics, Brain, Brain Stem Clinical trial registration no. NCT02811263 Supplemental material is available for this article. © RSNA, 2024 See also commentary by Rafful and Reis Teixeira in this issue.

Keywords: Brain; Brain Stem; Convolutional Neural Network (CNN); Pediatrics; Prognosis.

Graphical abstract

Figure 1:

Flow diagram for participants included in each subset. Participants were enrolled as part of the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study from 17 different institutions within the United States. The out-of-distribution test set contains all participants from two institutions. Participants from the remaining 15 institutions were randomly split into a training set, validation set, and in-distribution test set with an equal distribution of institutions in each subset.

Figure 2:

Example MR images for a participant with moderate neurodevelopmental impairment (NDI) (top row) and no death or NDI (bottom row) at 2-year follow-up. Neither set of images reveals any definite focal areas of brain injury, which highlights the difficulty in prognostication based on MRI. ADC = apparent diffusion coefficient.

Figure 3:

Flowchart of (A) imaging preprocessing steps and (B) model architecture overview. *Only T1- and T2-weighted images underwent bias correction.

Figure 4:

Graph of receiver operating characteristic curves for predictions in the in-distribution and out-of-distribution test sets. The Outcome Prediction in Neonates with Encephalopathy (OPiNE) model used T1-weighted, T2-weighted, apparent diffusion coefficient, trace, and readily available clinical tabular data to perform predictions. The tabular data–only model used logistic regression on the tabular data only. Radiologist scoring used the same imaging sequences as the OPiNE model. All methods were compared using the DeLong method, and there was a difference between the OPiNE and tabular data–only model in the out-of-distribution test set. All other comparisons demonstrated no difference. AUC = area under the receiver operating characteristic curve.

Figure 5:

Gradient-weighted class activation mapping overlaid on T1-weighted, T2-weighted, apparent diffusion coefficient (ADC), and trace images at the level of the basal ganglia and thalami for cases of death or neurodevelopmental impairment (NDI, top row) and no death or NDI (bottom row). Gradients of the final convolutional layer were scaled between 0 and 1 and demonstrate salient areas of the image used in classification. Gestational age and sex for each neonate included in the figure, from left to right, are: upper row, 39-week male, 36-week male, 36-week male, and 39-week male; lower row, 40-week male, 39-week female, 41-week male, and 40-week female.