Abstract: System, device, and method of determining Anisomelia or Leg Length Discrepancy (LLD) of a subject, by using image analysis and machine learning. A system includes a plurality of end-user devices; each device includes a camera to capture digital non-radiological non-X-Ray photographs of legs of a person; each device further includes a local Deep Neural Network (DNN) engine to perform local classification of images as either manifesting LLD or non-manifesting LLD. The digital non-radiological non-X-Ray photographs are also uploaded from the end-user devices to a central server, which updates and upgrades the DNN model based on transfer learning, and periodically distributes the upgraded DNN model downstream to the end-user devices.

Abstract: System, device, and method of determining Anisomelia or Leg Length Discrepancy (LLD) of a subject, by using image analysis and machine learning. A system includes a plurality of end-user devices; each device includes a camera to capture digital non-radiological non-X-Ray photographs of legs of a person; each device further includes a local Deep Neural Network (DNN) engine to perform local classification of images as either manifesting LLD or non-manifesting LLD. The digital non-radiological non-X-Ray photographs are also uploaded from the end-user devices to a central server, which updates and upgrades the DNN model based on transfer learning, and periodically distributes the upgraded DNN model downstream to the end-user devices.

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).

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).