A computer implemented method is provided for processing a 3D fetal ultrasound image. A 3D fetal ultrasound image is obtained (either acquired or received from memory), and the spine is detected within the image. This enables a first reference axis to be defined. A second reference axis is defined perpendicular to the first reference axis, and the 3D fetal ultrasound image is updated (e.g. rotated in 3D space) using the first and second reference axes and an up/down (elevation) orientation detection. This provides a normalization of the orientation of the image, so that a machine learning approach is better able to identify landmarks within new images.

A machine-based learning method estimates a probability of bone fractures in a 3D image, more specifically vertebral fractures. The method and system utilizing such method utilize a data-driven computational model to learn 3D image features for classifying vertebra fractures. A three-dimensional medical image analysis system for predicting a presence of a vertebral fracture in a subject includes a 3D image processor for receiving and processing 3D image data of a 3D image of the subject, producing two or more sets of 3D voxels. Each of the sets of 3D voxels corresponds to an entirety of the 3D image and each of the sets of 3D voxels consists of equal 3D voxels of different dimensions. The system also includes a voxel classifier for assigning the 3D voxels one or more class probabilities each of the 3D voxels contains a fracture using a computational model, and a fracture probability estimator for estimating a probability of the presence of a vertebral fracture in the subject.

Methods and systems for performing computer-assisted image-guided surgery, including robotically-assisted surgery. A method of displaying image data includes displaying image data of a patient on a handheld display device, tracking the handheld display device using a motion tracking system, and modifying the image data displayed in response to changes in the position and orientation of the handheld display device. Further embodiments include a sterile case for a handheld display device, display devices on a robotic arm, and methods and systems for performing image-guided surgery using multiple reference marker devices fixed to a patient.

Disclosed are systems and methods for rapid generation of simulations of a patient's spinal morphology that enable pre-operative viewing of a patient's condition and to assist surgeons in determining the best corrective procedure and with any of the selection, augmentation or manufacture of spinal devices based on the patient specific simulated condition. The simulation is generated by morphing a generic spine model with a three-dimensional curve representation of the patient's particular spinal morphology derived from existing images of the patient's condition.

Methods and systems for detecting a vertebral fracture within an x-ray. One method includes receiving a chest x-ray image and identifying a plurality of vertebrae represented in the chest x-ray image. The method further includes extracting a plurality of image patches from the chest x-ray image, each image patch of the plurality of image patches including a portion of the chest x-ray image representing one of the plurality of vertebrae identified in the chest x-ray image. The method further includes sequencing the plurality of image patches into an ordered sequence of image patches, and assigning, with a deep learning model applied to the ordered sequence of image patches, a classification to each of the plurality of image patches indicating whether the image patch represents a fractured vertebra or an unfractured vertebra.

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.

A method of generating a virtual MRI image includes receiving an initial MRI image of a patient in a first position, the initial MRI image depicting at least a portion of an anatomy of the patient; receiving a plurality of images of the patient in a second position, each of the plurality of images depicting at least a plurality of bony elements within the portion of the patient's anatomy; receiving soft tissue information corresponding to at least a plurality of soft tissue elements within the portion of the patient's anatomy; segmenting the initial MRI image to identify a depiction of each of the plurality of bony elements and each of the plurality of soft tissue elements within the initial MRI image; and generating a virtual MRI image based on the MRI image, the plurality of images, and the soft tissue information.

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 method for supporting medical interventions using an augmented reality system is disclosed, which comprises determining, using a position marker and an electronic tracking system, positions of a set of points; calculating a geometric shape using the determined positions of the set of points; and displaying, on an optical head mounted display the calculated geometric shape in the field of view of a bearer of the display. Furthermore, a system and a position marker adapted to be used for this method are disclosed.

This invention provides systems and methods that can detect incidental OVFs in CT examinations at the level of practicing radiologists. The OVF detection system leverages a deep convolutional neural network (CNN) to extract radiological features from each CT scan slice. These extracted features are processed through a feature aggregation module to make the final diagnosis for the full CT scan. Feature aggregation, can be performed in a variety of ways, including the use of a long short-term memory (LSTM) network. In one example, the CNN can be trained on a predetermined number of CT scans, which are established as reference standards. This result can effectively the performance of practicing radiologists on this test set in real world clinical circumstances. The system and method can be employed to assist and improve OVF diagnosis in clinical settings by pre-screening routine CT examinations and flagging suspicious cases prior to review by radiologists.