Abstract: Disclosed is a medical imaging conversion method, automatically converting: at least one or more real x-ray images of a patient, including at least a first anatomical structure of the patient and a second anatomical structure of the patient, into at least one digitally reconstructed radiograph (DRR) of the patient representing the first anatomical structure without representing the second anatomical structure, by a single operation using either one convolutional neural network (CNN) or a group of convolutional neural networks (CNN) which is preliminarily trained to, both or simultaneously: differentiate the first anatomical structure from the second anatomical structure, and convert a real x-ray image into at least one digitally reconstructed radiograph (DRR).

Abstract: The present document describes a method for producing a knee joint treatment plan and/or surgery plan for a patient, the method comprising: obtaining 3D kinematic data of the knee joint in movement; determining, from the 3D kinematic data, scores characterizing the joint function of the patient, the one or more scores being relative to one or more criteria; and comparing the scores to data in a database which characterize a plurality of treatment plans and/or surgery plans to generate the list of one or more treatment plans and/or surgery plans which match the scores.

Abstract: Method for reconstruction of a three-dimensional model of an osteo-articular structure of a patient, wherein a) bi-dimensional patient-specific image data (41) of said structure is provided; (d) a preliminary solution, corresponding to a previously established solution model of the structure, is provided (42) from a base (21), said preliminary solution comprising a priori knowledge of the corresponding structure, previously established from structures of the same type, said preliminary solution comprising surface data describing the coordinates of the surface of the solution model, and bulk data describing at least one characteristic of the inside of the solution model; the preliminary solution is modified (42?, 43, 44, 46, 47, 48) to be brought in concordance with said patient-specific image data.

Abstract: Method for reconstruction of a three-dimensional model of an osteo-articular structure of a patient, wherein a) bi-dimensional patient-specific image data (41) of said structure is provided; (d) a preliminary solution, corresponding to a previously established solution model of the structure, is provided (42) from a base (21), said preliminary solution comprising a priori knowledge of the corresponding structure, previously established from structures of the same type, said preliminary solution comprising surface data describing the coordinates of the surface of the solution model, and bulk data describing at least one characteristic of the inside of the solution model; the preliminary solution is modified (42?, 43, 44, 46, 47, 48) to be brought in concordance with said patient-specific image data.

Abstract: An integrated multi-step computer-implemented system and method for measuring and improving manufacturing processes and maximizing product research and development speed and efficiency includes a predictive model that predicts output from data input, an optimizer that optimizes input variables based upon desired output variables, and a library that stores data and information. The system further includes an artificial intelligence that receives requests and information from manufacturers and customers, and directs the requests and information to the predictive model if an output prediction is requested, to the optimizer if an optimized input is requested, or to the library if the requests cannot be answered by the predictive model or optimizer. The predictive model, the optimizer, and the library all interconnect with the artificial intelligence. The system further includes a high-throughput screening system that analyzes various material combinations and sends data to the library.

Abstract: An integrated multi-step computer-implemented system and method for measuring and improving manufacturing processes and maximizing product research and development speed and efficiency is disclosed. The system includes a predictive model that predicts output from data input, an optimizer that optimizes input variables based upon desired output variables, and a library that stores data and information. The system further includes an artificial intelligence that receives requests and information from manufacturers and customers, and directs the requests and information to the predictive model if an output prediction is requested, to the optimizer if an optimized input is requested, or to the library if the requests cannot be answered by the predictive model or optimizer. The predictive model, the optimizer, and the library all interconnect with the artificial intelligence. The system further includes a high-throughput screening system that analyzes various material combinations and sends data to the library.