Radiation-Less Monitoring of Scoliosis Based on Single Back Photographs Using Smartphones and an Open System Powered by Deep Learning: System Development and Longitudinal Evaluation (Preprint) (original) (raw)

Abstract

BACKGROUND Routine check-ups for adolescent idiopathic scoliosis are critical to monitor progression and prescribe interventions. AIS is primarily screened via physical examination. If there are features of deformity, radiographs are necessary for diagnosis or follow-up, guiding further management, i.e., bracing corrections for moderate deformities and spine surgeries for severe deformities. However, this subjects children to repetitive radiation and routine practices can be disturbed. OBJECTIVE We aim to develop and prospectively validate an open mobile platform powered by validated deep learning models known as ScolioNets, for AIS severity and curve type classifications as well as progression identifications, to facilitate timely management of AIS with no extra radiation exposure. METHODS During the technology development stage, ScolioNets was trained and validated by 1780 back photos, consisting of heterogeneous severities and curve types. The ground truth (GT) labels for severit...

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