Evaluation of Infants with Spinal Muscular Atrophy Type-I Using Convolutional Neural Networks
Spinal Muscular Atrophy is the most common genetic cause of infant death. Due to its severity, there is a need for methods for automated estimation of disease progression. In this paper we propose a Convolutional-Neural-Network (CNN) model to estimate dis
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Mechanical Engineering Department, University of Washington, Seattle, USA [email protected], [email protected] Clinical Therapies Department, Nationwide Children’s Hospital, Columbus, USA [email protected]
Abstract. Spinal Muscular Atrophy is the most common genetic cause of infant death. Due to its severity, there is a need for methods for automated estimation of disease progression. In this paper we propose a Convolutional-Neural-Network (CNN) model to estimate disease progression during infants’ natural behavior. With the proposed methodology, we were able to predict each child’s score on current behaviorbased clinical exams with an average per-subject error of 6.96 out of 72 points (
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