Abstract: A method for optimizing orthopedic spinal implant survival using preoperative finite element analysis combined with intraoperative stress analysis. Based on clinically relevant data, finite element analysis, and corrected values of spinal parameters, an acceptable long-term stress score is determined for an appropriate implant, which is selected from a set of potential implants, such that the shape of the implant minimizes predicted stress values. From a preoperative medical image set, values of selected spinal alignment parameters are determined; finite element analysis is performed on potential implants to determine stress values; and a selected implant is digitally positioned in the medical image set to create a virtual bone/implant configuration. After the selected implant is inserted and bent to shape, actual stress values are measured intraoperatively.

Abstract: A method for early diagnosis of an autoimmune or chronic disease in subject. The method includes (i) selecting, out of missing existing health related data of the subject (HRDS) items, a subject-specific subset; (ii) obtaining at least one missing existing HRDS item; (iii) adding the at least one obtained HRDS item to an existing HRDS to provide an updated HRDS; (iv) applying to the updated HRDS, a second machine learning model adapted to convert parameters of the updated HRDS, some of which may be indicative of the early development stages of the disease, into a second vector that provides a compact representation of the updated HRDS that reflects on the medical condition of the subject; and (v) applying a second classifier model to the second vector to provide a second classification result that is indicative of a second likelihood of the subject having or developing the disease.

Abstract: A method for determining an acceptable spinal surgical plan for a subject using pathology prediction, comprising generating a potential spinal surgical plan, obtaining clinically relevant data of the subject, obtaining pre-operative three-dimensional images of a spinal region of the subject, determining relationships between pairs of vertebrae in the images, predicting relationships between pairs of vertebrae that are expected from the surgical plan, accessing a multiple patient database, obtaining sets of data from the database for patients with similar characteristics to the subject, determining risks of pathology types for the subject, using artificial intelligence to combine the determined risks to calculate an overall risk for pathology types for the subject, and if the overall risks are unacceptable, selecting an alternative spinal surgical plan, and if the said overall risks are acceptable, determining that said surgical plan is acceptable.

Abstract: An exemplary method of determining a surgical spinal correction for a subject using analysis of motion capture images of the subject, which uses the steps of obtaining pre-operative three-dimensional images of a spinal region, obtaining a pre-operative time sequenced set of images of the subject during a movement progression of said subject, calculating in a plurality of the motion capture images, alignment parameters relating to upper and lower body regions of the subject, and determining if any of the calculated alignment parameters are outside their predetermined acceptable ranges in one or more of the images, iteratively adjusting anatomical elements in three-dimensional images until all of the calculated alignment parameters are within their predetermined acceptable ranges; and adjusting spinal anatomy in the three-dimensional images according to the degree of adjustment of spinal parameters in the motion capture images to determine a surgical spinal correction.

Abstract: An exemplary method of determining a surgical spinal correction for a subject using analysis of motion capture images of the subject, which uses the steps of obtaining pre-operative three-dimensional images of a spinal region, obtaining a pre-operative time sequenced set of images of the subject during a movement progression of said subject, calculating in a plurality of the motion capture images, alignment parameters relating to upper and lower body regions of the subject, and determining if any of the calculated alignment parameters are outside their predetermined acceptable ranges in one or more of the images, iteratively adjusting anatomical elements in three-dimensional images until all of the calculated alignment parameters are within their predetermined acceptable ranges; and adjusting spinal anatomy in the three-dimensional images according to the degree of adjustment of spinal parameters in the motion capture images to determine a surgical spinal correction.

Abstract: A method of planning the correction of spinal deformations of a subject, by performing segmentation on a three dimensional image of the subject's spine in its erect neutral position, such that the positions and orientations of the vertebrae in a region of interest are characterized. Parameters relating to the alignment and position of the vertebrae are derived from the segmentation, followed by determining whether the parameters fall within an acceptable range desired for the spine of the subject. If not within the acceptable range, an alignment optimization is performed on the vertebrae to bring the parameters within the acceptable range, to reduce the spinal deformations of the subject's spine. The alignment optimization is performed by taking into consideration limitations arising from the dynamic range of motion of the vertebrae as determined by analyzing images of the subject's spine, while the subject is in positions of maximum bending.

Abstract: Methods enabling prediction, screening, early diagnosis, and recommended intervention or treatment selection of autoimmune conditions using artificial intelligence operating in conjunction with large medical datasets. Logic is applied to historic population data to extract medical features and identify subjects with diagnosed autoimmune conditions, and the pre-diagnosis medical data is used to train a diagnosis classification algorithm. A self-supervised learning mechanism is separately used to generate a feature embedding transformation of the patients medical history into representational feature vectors. These patient feature vectors together with their expected diagnoses are used to train a multi-label classifier model using supervised learning. The embedding transformation and the multi-label classifier are then applied to a current subjects data to generate a patient diagnosis probability vector, predicting the existence of autoimmune conditions.

Abstract: Methods enabling prediction, screening, early diagnosis, and recommended intervention or treatment selection of chronic medical conditions using artificial intelligence operating in conjunction with large medical datasets. Logic is applied to historic population data to extract medical features and identify subjects with diagnosed chronic conditions, and the pre-diagnosis medical data is used to train a diagnosis classification algorithm. A self-supervised learning mechanism is separately used to generate a feature embedding transformation of the patient's medical history into representational feature vectors. These patient feature vectors together with their expected diagnoses are used to train a multi-label classifier model using supervised learning. The embedding transformation and the multi-label classifier are then applied to a current subject's data to generate a patient diagnosis probability vector, predicting the existence of chronic conditions.

Abstract: A method for optimizing orthopedic spinal implant survival using preoperative finite element analysis combined with intraoperative stress analysis. Based on clinically relevant data, finite element analysis, and corrected values of spinal parameters, an acceptable long-term stress score is determined for an appropriate implant, which is selected from a set of potential implants, such that the shape of the implant minimizes predicted stress values. From a preoperative medical image set, values of selected spinal alignment parameters are determined; finite element analysis is performed on potential implants to determine stress values; and a selected implant is digitally positioned in the medical image set to create a virtual bone/implant configuration. After the selected implant is inserted and bent to shape, actual stress values are measured intraoperatively.

Abstract: A method of planning the correction of spinal deformations of a subject, by performing segmentation on a three dimensional image of the subject's spine in its erect neutral position, such that the positions and orientations of the vertebrae in a region of interest are characterized. Parameters relating to the alignment and position of the vertebrae are derived from the segmentation, followed by determining whether the parameters fall within an acceptable range desired for the spine of the subject. If not within the acceptable range, an alignment optimization is performed on the vertebrae to bring the parameters within the acceptable range, to reduce the spinal deformations of the subject's spine. The alignment optimization is performed by taking into consideration limitations arising from the dynamic range of motion of the vertebrae as determined by analyzing images of the subject's spine, while the subject is in positions of maximum bending.

Abstract: A method of planning the correction of spinal deformations of a subject, by performing segmentation on a three dimensional image of the subject's spine in its erect neutral position, such that the positions and orientations of the vertebrae in a region of interest are characterized. Parameters relating to the alignment and position of the vertebrae are derived from the segmentation, followed by determining whether the parameters fall within an acceptable range desired for the spine of the subject. If not within the acceptable range, an alignment optimization is performed on the vertebrae to bring the parameters within the acceptable range, to reduce the spinal deformations of the subject's spine. The alignment optimization is performed by taking into consideration limitations arising from the dynamic range of motion of the vertebrae as determined by analyzing images of the subject's spine, while the subject is in positions of maximum bending.

Abstract: A method for determining an acceptable spinal surgical plan for a subject using pathology prediction, comprising generating a potential spinal surgical plan, obtaining clinically relevant data of the subject, obtaining pre-operative three-dimensional images of a spinal region of the subject, determining relationships between pairs of vertebrae in the images, predicting relationships between pairs of vertebrae that are expected from the surgical plan, accessing a multiple patient database, obtaining sets of data from the database for patients with similar characteristics to the subject, determining risks of pathology types for the subject, using artificial intelligence to combine the determined risks to calculate an overall risk for pathology types for the subject, and if the overall risks are unacceptable, selecting an alternative spinal surgical plan, and if the said overall risks are acceptable, determining that said surgical plan is acceptable.

Abstract: An exemplary method of determining a surgical spinal correction for a subject using analysis of motion capture images of the subject, which uses the steps of obtaining pre-operative three-dimensional images of a spinal region, obtaining a pre-operative time sequenced set of images of the subject during a movement progression of said subject, calculating in a plurality of the motion capture images, alignment parameters relating to upper and lower body regions of the subject, and determining if any of the calculated alignment parameters are outside their predetermined acceptable ranges in one or more of the images, iteratively adjusting anatomical elements in three-dimensional images until all of the calculated alignment parameters are within their predetermined acceptable ranges; and adjusting spinal anatomy in the three-dimensional images according to the degree of adjustment of spinal parameters in the motion capture images to determine a surgical spinal correction.

Abstract: A method of planning the correction of spinal deformations of a subject, by performing segmentation on a three dimensional image of the subject's spine in its erect neutral position, such that the positions and orientations of the vertebrae in a region of interest are characterized. Parameters relating to the alignment and position of the vertebrae are derived from the segmentation, followed by determining whether the parameters fall within an acceptable range desired for the spine of the subject. If not within the acceptable range, an alignment optimization is performed on the vertebrae to bring the parameters within the acceptable range, to reduce the spinal deformations of the subject's spine. The alignment optimization is performed by taking into consideration limitations arising from the dynamic range of motion of the vertebrae as determined by analyzing images of the subject's spine, while the subject is in positions of maximum bending.