Abstract: A system and method configured to better identify patient-specific anatomical landmarks, measure anatomical parameters and features, and predict the patient's need for surgery within a predetermined time period. In some embodiments, the system and method is configured to predict the likelihood or risk that a patient will require total hip arthroplasty. In some embodiments, the present invention includes machine learning technology Some embodiments of the present invention include a first ML machine configured to received medical images as inputs and identify anatomical landmarks as outputs; a measurement module to measure joint space width, hip dysplasia angles, and/or leg length differential; and a second ML machine configured to receive the anatomical measurements and patient demographic data as inputs and produce a risk or likelihood that the patient will require surgery within a certain time frame.

Abstract: A system and method for determining patient-specific anatomical parameters to improve surgical outcomes. Some embodiments include processes for predicting the parameters of occluded anatomy. Some embodiment includes processes for more accurately identifying a center point of a ball and socket joint, such as a center point or center of rotation of a femoral head. Some embodiments include processes for identify a patient-specific spinal curvature, including more precisely determining patient specific spinal inflection points. The various steps can be performed automatically through trained computing devices and graphically presented to a surgeon for review and any necessary modifications.

Abstract: A system and method configured to better identify patient-specific anatomical landmarks, measure anatomical parameters and features, and predict the patient's need for surgery within a predetermined time period. In some embodiments, the system and method is configured to predict the likelihood or risk that a patient will require total hip arthroplasty. In some embodiments, the present invention includes machine learning technology Some embodiments of the present invention include a first ML machine configured to received medical images as inputs and identify anatomical landmarks as outputs; a measurement module to measure joint space width, hip dysplasia angles, and/or leg length differential; and a second ML machine configured to receive the anatomical measurements and patient demographic data as inputs and produce a risk or likelihood that the patient will require surgery within a certain time frame.

Abstract: A system and method for determining patient-specific anatomical parameters to improve surgical outcomes. Some embodiments include processes for predicting the parameters of occluded anatomy. Some embodiment includes processes for more accurately identifying a center point of a ball and socket joint, such as a center point or center of rotation of a femoral head. Some embodiments include processes for identify a patient-specific spinal curvature, including more precisely determining patient specific spinal inflection points. The various steps can be performed automatically through trained computing devices and graphically presented to a surgeon for review and any necessary modifications.