According to the invention, the process includes the steps of: a) taking a sagittal preoperative x-ray of the vertebral column of the patient to be treated, extending from the cervical vertebrae to the femoral heads; b) on that x-ray, identifying points on S1, S2, T12 et C7; c) depicting, on the said x-ray, curved segments beginning at the center of the plate of S1 et going to the center of the plate of C7; e) identifying, on that x-ray, the correction(s) to be made to the vertebral column, including the identification of posterior osteotomies to make; f) pivoting portions of said x-ray relative to other portions of that x-ray, according to osteotomies to be made; g) performing, on said x-ray, a displacement of the sagittal curvature segment extending over the vertebral segment to be corrected; h) from a straight vertebral rod (TV), producing the curvature of that rod according to the shape of said sagittal curvature segment in said displacement position.

An approach for a computer program to receive image data of a subject including at least a portion of a spine of the subject and a chronological age of the subject. The approach includes the computer program pre-processing the image data including at least a portion of a spine. The approach includes determining an apparent age of the spine or a portion of the spine of the subject using a trained artificial intelligence deep learning algorithm.

A low radiation, intra-operative method using two-dimensional imaging to register the positions of surgical implants relative to their pre-operative planned positions. Intraoperatively, a pair of two-dimensional fluoroscope images in different planes or a single three-dimensional image is acquired and compared to a set of three-dimensional preoperative images, to allow registration of the implant region anatomy. A second set of intraoperative fluoroscope images is acquired of the surgical area with implants in place. The second set of images is compared with the first set of intraoperative images to ascertain alignment of the implants. Registration between first and second intraoperative image sets is accomplished using the implants themselves as fiducial markers, and the process repeated until an acceptable configuration of the implants is obtained. The method is particularly advantageous for spinal surgery.

A method of generating a quantitative characterization of injury presence and status of spinal cord tissue using an adaptive CNN system for use in diagnostic assessment, surgical planning, and therapeutic strategy comprises preprocessing for artifact correction of diffusion based, spinal cord MM data, training an adaptive CNN system with healthy and abnormal (injured/pathologic) spinal cord images obtained by imaging a population of healthy, typically developed spinal cord subjects and subjects with spinal cord injury, evaluating a novel, diffusion-based MM image for injury biomarkers using the adaptive CNN system, generating a three-dimensional predictive axonal damage map for quantitative characterization and visualization of the novel, diffusion-based MM image, and transmitting the sets of healthy and injured spinal cord images back to a central database for continued improvement of the adaptive CNN system training. A system for defining a predictive spinal axonal damage map is also described.

Systems, devices, and methods for identifying a region of interest are provided. A plurality of skeletal landmarks may be identified from an image received from an imaging device. A pose of a patient may be determined based on the plurality of skeletal landmarks. A region of interest may be identified on the patient based on the determined pose. Instructions may be automatically provided to the controller to adjust a pose of a surgical instrument relative to the region of interest. The plurality of skeletal landmarks may be tracked for movement. The region of interest may be updated when movement of the plurality of skeletal landmarks is detected.

Systems and methods are disclosed whereby a surface detection system is employed to obtain intraoperative surface data characterizing an exposed surface of the spine. In some embodiments, this intraoperative surface data is registered to segmented surface data obtained from volumetric data of the spine in order to assess the intraoperative orientation of the spine and provide feedback associated with the intraoperative orientation of the spine. The feedback may characterize the intraoperative spinal orientation as a change relative to the preoperative orientation.

A system for surgical planning and assessment of spinal deformity correction is provided that has a spinal imaging system and a control unit. The spinal imaging system is configured to collect at least one digitized position of one or more vertebral bodies of a subject. The control unit is configured to receive the at least one digitized position, and calculate, based on the at least one digitized position, an optimized posture for the subject. The control unit is configured to receive one or more simulated spinal correction inputs, and based on the inputs and optimized posture, predict an optimal simulated postoperative surgical correction.

The present disclosure relates to the generation of partial surface models them volumetric datasets for subsequent registration of such partial surface models to surface topology datasets. Specifically, given an object that is imaged using surface topology imaging and another volumetric modality, the volumetric dataset is processed in combination with an approach viewpoint to generate one or more partial surfaces of the object that will be visible to the surface topology imaging system. This procedure can eliminate internal structures from the surfaces generated from volumetric datasets, thus increases the similarity of the dataset between the two different modalities, enabling improved and quicker registration.

Disclosed is a navigation system. The navigation system may be used to at least assist in a procedure. The system may assist in delineating objects and/or determining physical boundaries of image elements. The system may assist in planning and/or a workflow of the procedure.

Automated determination of muscle mass from images can be carried by performing a thresholding process to an image file to generate a contrasted image, and segmenting pixels of the contrasted image into bone and not bone. The system can distinguish muscle from organ for the pixels segmented as not bone by determining a location of a rib cage of the patient using the pixels segmented as bone, and removing pixels segmented as not bone that are located within the location of the rib cage. The system can calculate a volume of muscle based on remaining pixels segmented as not bone; calculate a total muscle mass based on the volume of muscle; and provide the total muscle mass of the patient. The total muscle mass of the patient can then be used for applications including calculating a glomerular filtration rate.