Self-Tuning Device Framework for Analysis of Infant Cranial Development

Abstract

Infant cranial development is a very sensitive process that can involve abnormalities such as premature ossification which require corrective surgeries. Although regular checkups are suggested, the earlier these conditions are caught the more easily they are often treated. We propose a point-of-care diagnostic system that utilizes a self-tuning framework of sensory inputs and machine learning in order to detect abnormalities without a trained physician, resulting in earlier intervention.