Developing statistical image analysis tools for non-invasive monitoring of anemia in low birth weight infants

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Project Summary Our proposal is motivated by the need to develop non-invasive tools for monitoring anemia in very low birth weight (VLBW; birth weight < 1,500 grams) and reduce the number of routine painful, invasive blood sampling procedures (phlebotomy) that may alter infant neurodevelopment and behavior. Recently, a new smartphone application [Mannino et al., Nature Communications, 9, 4924 (2018)] that collects and analyzes clinical pallor in patient-sourced fingernail photos and image metadata has been developed to predict hemoglobin levels. The app uses a robust multi-linear regression model that incorporates summary color intensity values (average across pixels) of fingernail photos well as the image metadata generated by the device capturing the image to predict patient's hemoglobin level. While the current app algorithm is simple and easy to implement, there are notable limitations. First, it does not fully leverage the rich spatial information available in fingernail photos by calculating a simple average value. Second, the current algorithm is trained using only adults, whose clinical characteristics are vastly different from infants. The 95% limit of agreement between the app-predicted and blood sample-based hemoglobin level for adults is reported as 2.4 g/dL, which is higher than the Clinical Laboratory Improvement Amendments specification variance of 1.0 g/dL, and will likely increase in VLBW infants given their tiny, non-specific fingernail beds. Such strict error requirements and heterogeneity in populations demand more accurate and tailored algorithms than what the current app employs. Lastly, a framework for applying the app to minimize blood draws across the longitudinal care continuum for VLBW infants is currently lacking. With these considerations, we propose (Aim 1) to develop a new image analysis algorithm (IAA) that produces non-invasive, accurate and stable prediction of hemoglobin level. The IAA will be based on a novel principal component analysis method that provides a non-parametric and parsimonious means to jointly model high- dimensional photos and image metadata, while fully leveraging their spatial structures and co-varying patterns. We will also consider a new partial least squares approach as an alternative method. We will train and validate the IAA based on adult data as well as VLBW infant data. In Aim 2, we will develop a new clustering method to study sub-population structures of fingernail photos and image metadata and study their relationships with the underlying physiological mechanisms of anemia. This approach will allow us to formulate a non-invasive image- based screening tool by identifying clusters of VLBW infants with high anemia risk. In Aim 3, we will develop data-driven tools that leverage longitudinal, patient-level clinical data and IAA predictions to achieve the overarching clinical goal of minimizing the number of blood draws in VLBW infants throughout the care continuum. Our proposal will use the data of VLBW infants monitored at three level III neonatal intensive care units in Atlanta. The proposed methods are generally applicable to a wide variety of settings with diverse and complex modalities of clinical data.