Abstract: Systems, methods, and computer-readable storage media for vertebra bone density analysis, and more specifically to identifying which portions of vertebra have sufficient density to support different types of spinal surgery. A system can segment the pre-operation medical images and generate a 3D pre-operation model having a plurality of volumetric regions based on those pre-operation medical images, wherein each volumetric region has a bone density. The system can then identify different spine surgery options and generate, for each of the plurality of spine surgery options using the 3D pre-operation model, a 3D predicted model. The system can then simulate these predicted models being exposed to various stresses and forces, and predict which of the predicted models results in the best surgery outcome.

Abstract: Systems, methods, and computer-readable storage media for measuring spinal canal volume in vertebrates, and more specifically to using Artificial Intelligence (AI) to predict how surgical options will affect spinal canal volume. A system configured as disclosed herein can receive two or more pre-operation medical images capturing at least one functional spinal unit, then calculate an initial spinal canal regional volume using the pre-operation medical images for at least a portion of the at least one functional spinal unit. The system can then calculate, using a neural network, a predicted spinal canal regional volume for at least a portion of the at least one functional spinal unit undergoing various spine surgery options separately, resulting in predicted spinal canal regional volumes corresponding to the plurality of spine surgery options. The system or a surgeon can then select, using that data, one or more of the spine surgery options.

Abstract: Methods and systems for evaluating the biomechanical properties of a subject's spine for the purpose of optimizing a potential surgical intervention by selecting or designing surgical hardware for a spinal correction procedure. The system uses a simulated dynamic analysis configured to analyze, predict and improve the outcome of the surgical procedure by taking into account physiological, biomechanical, and anatomical factors. Machine learning algorithms use the computed distribution of forces and moments on the subject's spine, and compare the subject's results with those of patients from a reference population.

Abstract: Methods and systems for evaluating the biomechanical properties of a subject's spine for the purpose of optimizing a potential surgical intervention by selecting or designing surgical hardware fora spinal correction procedure. The system uses a simulated dynamic analysis configured to analyze, predict and improve the outcome of the surgical procedure by taking into account physiological, biomechanical, and anatomical factors. Machine learning algorithms use the computed distribution of forces and moments on the subject's spine, and compare the subject's results with those of patients from a reference population.

Abstract: Methods and systems for evaluating the biomechanical properties of a subject's spine for the purpose of optimizing a potential surgical intervention by selecting or designing surgical hardware fora spinal correction procedure. The system uses a simulated dynamic analysis configured to analyze, predict and improve the outcome of the surgical procedure by taking into account physiological, biomechanical, and anatomical factors. Machine learning algorithms use the computed distribution of forces and moments on the subject's spine, and compare the subject's results with those of patients from a reference population.