Image processing and analysis technique includes using a computer apparatus to assess a patient's computed tomography (CT) or magnetic resonance images for pathology and then automatically generate a prescription based at least in part on that assessment.

A spinal surgery training process includes the steps of capturing a plurality of 2D images for each of a plurality of spines, generating a curve of each spine from the respective 2D images based on locations of select vertebrae in each of the spines, grouping the spines into one of a number of groups based on similarity to produce groups of spines having similarities, performing the capturing, generating, determining and grouping steps at least once prior to surgery and at least once after surgery to produce pre-operative groups and their resultant post-operative groups, and assigning surgical methods and a probability to each of the post-operative groups indicating the probability that a spinal shape of the post-operative group can be achieved using the surgical methods. An outcome prediction process for determining surgical methods can be implemented once the training process is complete.

The present invention relates to a device for modeling cervical artificial disc based on artificial intelligence and a method thereof, comprising generating and outputting a model of a target cervical artificial disc, by generating a learning model to estimate positions of a plurality of landmarks through training various shapes of medical images with the plurality of landmarks assigned to define an available space in which the cervical artificial disc is located, estimating the plurality of landmarks by inputting input data generated from a medical image of the available space in which the target cervical artificial disc is to be positioned into the generated learning model, generating the available space for inserting the target cervical artificial disc based on estimated landmarks, and extracting and incorporating shape information for surfaces and edges of the cervical artificial disc from the medical image in the generated available space.

Described herein are various implementations of systems and methods for determining likelihood of a patient favorably responding to a neuromodulation procedure based on a quantitative or objective score or determination based on a plurality of indicators of pain (e.g., chronic low back pain stemming from one or more vertebral bodies or vertebral endplates of a patient). The systems and methods may involve application of artificial intelligence techniques (e.g., trained algorithms, machine learning or deep learning algorithms, and/or trained neural networks).

The disclosure relates to a computer-implemented method for determination of a bone cement volume of a bone cement for a percutaneous vertebroplasty, which reduces complications due to the information provided by the method. The computer-implemented method includes: reading in or receiving data about a bone structure of a bone section; determining a bone density distribution of the bone section depending on the data; determining a bone space in the bone section depending on the determined bone density distribution, wherein a bone density of the bone space is less than a predetermined density threshold value; determining a bone space volume of the determined bone space; and determining and outputting the bone cement volume depending on the determined bone space volume.

Disclosed are devices, systems and methods for using bilateral implant to restore spinal alignment and/or maximize range of motion for patients who suffer from spinal degenerative disorders.

Disclosed is a system to assist in a procedure. During the procedure an object may be moved relative to a subject, such as being positioned and/or placed within a subject. The system and related method may be used to assist in displaying and/or determining a pose of the object relative to a subject, such as rigid portions of a subject.

The invention provides an image segmentation method and an electronic device. The image segmentation method includes the following steps. Regression analysis is performed on a first gray-scale image to obtain a residual image having an object backbone area. A pixel value of each pixel in the object backbone area is defined as an average gray-scale value of the object backbone area in the residual image, and a second gray-scale image having the object backbone area is generated. It is recursively determined whether a residual polarity of each adjacent pixel adjacent to edge pixels of the object backbone area in the residual image is the same as a residual polarity of the corresponding edge pixel, and whether a pixel value of each adjacent pixel is greater than a first threshold, so as to expand the object backbone area in the second gray-scale image, which is extracted as a target object.

A method and system for automatically labeling a spine image is disclosed. The method includes receiving an input spine image and analyzing image features of the input spine image by a deep neural network. The method further includes generating a mask image corresponding to the input spine image by the deep neural network based on image characteristics of a training image dataset. A region of interest in the mask image comprises vertebral candidates of the spine. The training image dataset comprises a plurality of spine images and a plurality of corresponding mask images. The method further includes associating labels with a plurality of image components of the mask image and labeling the input spine image based on labels associated with the mask image.

Apparatus for performing a procedure using a tool configured to be advanced into a skeletal portion within a body of a subject along a longitudinal insertion path, and for use with:

(a) a 3D imaging device configured to acquire 3D image data of the skeletal portion.

(b) a 2D x-ray imaging device that is unregistered with respect to the subject's body and configured, while a portion of the tool is disposed at a first location along the longitudinal insertion path.