System and method for determining anisomelia condition of a subject using image analysis and deep neural network learning

Abstract

The present invention is an Deep Neural Network based technology relating to diagnosis of Anisomelia, also referred to as Leg Length Discrepancy (LLD). This invention is a system and method, which comprises of a diagnosis device referred to as the “LEG-Minder” device that is typically installed in a diagnosis center setting, and diagnoses for LLD on the basis of a neural network model with patient's leg photos or x-rays thereof; and a neural network learning server referred to as the “LEGislator” which is connected to the Internet and performs Deep Neural Network (DNN) learning, on the individual LLD databases generated by a plurality of the “LEG-Minder” device(s). In particular, the present invention relates to a technology in which patient's leg photos (or x-rays) and the corresponding diagnostic result data are acquired in each diagnosis center and then individually uploaded to the LEGislator; and then, on the basis of the uploaded information the LEGislator performs DNN learning to generate an upgraded neural network model, which is then disseminated to the “LEG-Minder” device(s), providing them the latest learnings, which subsequently helps in improving the diagnosis accuracy. This invention optimizes the diagnosis environment of the diagnosis center for Anisomelia.

Dependencies are inferred from the claim text.