A machine-based learning system for predicting the presence of lesions indicative of axial spondyloarthritis in medical imaging data includes an image processor for receiving and processing one or more MRI image data set containing pelvic region and sacroiliac joint of a subject, and dividing said MRI image data set into a set of 3D voxels. The system also includes a voxel classifier for calculating for each of said the voxels one or more class probabilities of such voxel to contain a lesion of a particular type or being a non-lesion. The system also includes a lesion probability map generator for receiving the data produced by the voxel classifier and producing a probability intensity map for each of lesion types, in which one or more areas classified as lesion are highlighted; and an image display for displaying the output of the lesion probability map generator.

An imaging method. The method comprises the following steps: determining a target by identifying target-related position information or characteristic information (S101); implementing a two-dimensional scan of the target to collect image data of the target in a three-dimensional space (S102); processing, during the scanning, and on a real-time basis, the image data and relevant spatial information to obtain a plurality of image contents of the target, and displaying the image content on a real-time basis (S103); and arranging the plurality of image contents in an incremental sequence to form an image of the target (S104). The imaging method prevents collection of unusable image information, shortens image data collection time, and increases the speed of an imaging process. The application further provides an imaging device.

Apparatus and methods are described including acquiring 3D image data of a targeted vertebra. A processor indicates the targeted vertebra within the 3D image data. A radiopaque element is positioned on the body of the subject with respect to the spine and a radiographic image is acquired. The processor (a) generates a plurality of 2D projections of the targeted vertebra from the 3D image data, (b) for each vertebra that is visible in the radiographic image, identifies if there exists a 2D projection of the targeted vertebra that matches the radiographic image of the vertebra that is visible, and (c) in response, indicates on the 2D radiographic image the vertebra for which a match with a projection of the targeted vertebra was identified, such that a location of the targeted vertebra is identified with respect to the radiopaque element. Other applications are also described.

A process operable using a computerized system for providing one or more output images of the spinal region of a subject for which anatomical landmarks applicable for clinical assessment are labeled in a pre-trained neural network (120a,120b) for clinical assessment of malalignment of a spine of a subject, the computerized system (100a,100b) including an image data acquisition device (110a), a pre-trained neural network (120a,120b) and an output module (140a) operably interconnected together via a communication link, said process including the steps of (i) by an image data acquisition device (110a,), acquiring one or more data input sets indicative of the spinal region of a subject, wherein each data input set of the one or more data input sets is indicative of an optical image of said subject at one or more corresponding postures of the subject; (ii) in a pre-trained neural network (120a,120b), providing labels to anatomical landmarks of said one or more data input sets each of which is indicative of said optical image of the subject at said one or more postures of the subject acquired during step (i) so as to provide one or more optical output images for subsequent clinical assessment of the spine of said subject, wherein the pre-trained neural network (120a,120b) has been pre-trained utilising one or more training data input sets corresponding to one or more predetermined postures of training subjects acquired from a plurality of training subjects, wherein said one or more predetermined postures are postures utilized for clinical assessment of malalignment of the spine of a subject; wherein the anatomical landmarks of the spine of said one or more training data input sets acquired from said plurality of training subjects have been pre-labeled by at least one clinician; and wherein the one or more postures of said subject for which the one or more data input sets of the subject acquired during step (i) correspond to one or more of said predetermined postures; and (iii) displaying by the output module (140a), the one or more optical output images of the spinal region of said subject having said labels provided thereto by the pre-trained neural network (120a,120b), for clinical assessment.

The subject of this invention is a system and method for distortion adaptation for use with an imaging grid alignment apparatus (analogue or digital) and method of intra-operative use for joint replacements, spine, trauma fracture reductions and deformity correction and implant placement/alignment. The system provides for real time dynamic position tracked distortion-adaption grid.

A method including receiving information about a pose of each of a plurality of fiducials positioned on or within a patient; causing an imaging device to generate a single image of a portion of the patient, the single image depicting at least a portion of each of the plurality of fiducials; determining, based on the information and the single image, a pose of one or more anatomical elements represented in the single image; and comparing the determined pose of the one or more anatomical elements to a predetermined pose of the one or more anatomical elements.

Mediated-reality imaging systems, methods, and devices are disclosed herein. In some embodiments, an imaging system includes (i) a camera array configured to capture intraoperative image data of a surgical scene in substantially real-time and (ii) a processing device communicatively coupled to the camera array. The processing device can be configured to synthesize a three-dimensional (3D) image corresponding to a virtual perspective of the scene based on the intraoperative image data from the cameras. The imaging system is further configured to receive and/or store preoperative image data, such as medical scan data corresponding to a portion of a patient in the scene. The processing device can register the preoperative image data to the intraoperative image data, and overlay the registered preoperative image data over the corresponding portion of the 3D image of the scene to present a mediated-reality view.

Apparatuses, systems, and techniques to train and apply a first machine learning model to identify a plurality of regions of interest within an input image, and to train and apply a plurality of second machine learning models to identify one or more objects within each region of interest identified by the first machine learning model.

Systems and methods for designing and implementing patient-specific surgical procedures and/or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

Systems and methods for planning a surgical procedure are provided. Information corresponding to an examination of a patient and an image of patient may be received. The information and the image may be inputted into an analytical model figured to identify a pathology location. A needed surgical modification of the patient anatomy may be automatically identified. At least a portion of an anatomical element in a three-dimensional model of the patient anatomy may be automatically labeled.