MRI and Deep Learning for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants

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Project Summary/Abstract About 100,000 very preterm infants (VPI; ≤32 weeks gestational age) are born every year in the United States. Up to 35% develop noteworthy neurodevelopmental deficits, thereby increasing their risk for poor educational, health, and social outcomes. Unfortunately, neurodevelopmental deficits cannot currently be reliably diagnosed until 3 to 5 years of age. The imminent challenge lies in early identification of infants that are more likely to develop later deficits. Advances in magnetic resonance imaging (MRI) and deep learning (DL) provide means to address this challenge. Application of DL to infant brain MRI data can open up new windows into early prediction of neurodevelopmental outcomes in at-risk infants and facilitate the move towards precision medicine. Our objective is to apply DL to MRI acquired at term equivalent age for early prediction of neurodevelopment deficits (cognitive, language, and motor) at age 2 in VPI. Our group has identified three key components necessary for accurate prognostic models of later neurodevelopment. DL analysis of 1) anatomical features derived from structural MRI (sMRI) allowing detection of brain structural abnormalities and tissue pathologies; 2) brain connectivity features derived from resting-state functional MRI (rs-fMRI) and diffusion MRI (dMRI) giving insights into atypical brain connectivity patterns; and 3) integration of anatomical and connectivity features, thus enhancing abnormal neurodevelopment prediction. In this project, we will dedicate our efforts in accomplishing the following specific aims. In Aim 1 and Aim 2, we will develop deepAna and deepConn models analyzing anatomical and connectivity features independently to predict adverse neurodevelopmental outcomes. By decoding each model, we will identify, validate and disseminate a series of the most discriminative anatomical and connectivity features to the research community. In Aim 3, we will develop an ensemble deepAnaConn model analyzing both anatomical and connectivity features, together with clinical risk factors, for early prediction of neurodevelopmental deficits. This model will help clinicians to predict later outcomes for those at-risk prematurely born infants before initial neonatal intensive care unit discharge. We will validate the models using both internal and independent external data and will open the ‘black-box’ of DL to aid interpretation of imaging and clinical findings. The techniques we develop are expected to improve the modelling fidelity in medical diagnosis/ prognosis in the same way as DL has revolutionized other fields. The DL models we develop will not only benefit early detection of neurodevelopmental deficits in VPI, but also likely benefit individuals with other neurodevelopmental and neurological diseases. This study will significantly impact public health because it will allow clinicians to target clinical and experimental intervention therapies to the most at-risk infants during periods of optimal neuroplasticity, and thus ultimately improve medical outcomes and patient well-being.