David V. Glidden

We propose an adaptable framework for analyzing ultrasound (US) images quantitatively to provide computer-aided diagnosis using machine learning. Our preliminary clinical targets are hepatic steatosis, adenomyosis, and craniosynostosis. For…

We propose a general ultrasound (US) texture-analysis and machine-learning framework for detecting the presence of disease that is suitable for clinical application across clinicians, disease types, devices, and operators. Its stages are im…