An artificial intelligence system for analyzing imagery, the system comprising a computing device, the computing device designed and configured to receive a plurality of photographs related to a human subject; analyze the plurality of photographs to identify a conditional indicator contained within the plurality of photographs; generate a classification algorithm utilizing the conditional indicator, wherein the classification algorithm utilizes the conditional indicator as an input and outputs a conditional profile; and determine a conditional status of the human subject utilizing the conditional profile.

A computer-implemented method to improve the point collection process during registration of a bone for a computer-assisted surgical procedure is provided. Based on bone digitization data, a simulation is performed to confirm the accuracy of the registration for different digitization regions. Results are tested to identify which digitization regions meet a predefined accuracy requirement. The resulting information is used to perform a computer-assisted surgical procedure. A computerized simulation method for registration of a bone for a computer-assisted surgical procedure is also provided based on processor executing random stroking an expected exposed surface of a bone model with multiple of stroke curves to cover most of the bone model surface with uniform noise and a random sample consensus is applied to remove outlying point to yield the best registration results, to find the top subset as to overlap. A method to perform computer-assisted surgery is also provided.

In accordance with one or more embodiments herein, a system for creating a decision support material indicating damage to at least a part of an anatomical joint of a patient, wherein the created decision support material comprises one or more damage images, is provided. The system comprises a storage media and at least one processor, wherein the at least one processor is configured to i) receive a series of radiology images of the at least part of the anatomical joint from the storage media; ii) obtain a three-dimensional image representation of the at least part of the anatomical joint which is based on at least a part of said series of radiology images, by generating said three-dimensional image representation in an image segmentation process based on said series of radiology images, or receiving said three-dimensional image representation from a storage media; iii) identify tissue parts of the anatomical joint in at least one of at least a part of said series of radiology images and/or the three-dimensional image representation using image analysis; iv) determine damage to the identified tissue parts in the anatomical joint by analyzing at least one of at least a part of said series of radiology images and/or the three-dimensional image representation of the at least part of the anatomical joint; v) determine suitable sizes and suitable implanting positions for one or more graft plugs based on the determined damage; vi) mark damage to the anatomical joint and suitable sizes and implanting positions for the one or more graft plugs in the obtained three-dimensional image representation of the anatomical joint; and vii) generate a decision support material, where the determined damage to the at least part of the anatomical joint and the suitable sizes and implanting positions for the one or more graft plugs are marked in at least one of the one or more damage images of the decision support material, and at least one of the one or more damage images is generated based on the obtained three-dimensional image representation of the at least part of the anatomical joint.

Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. A plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

Systems and methods may be used for evaluating a patient after completion of an orthopedic surgery on a portion of a body part of the patient. In an example, the method includes capturing, using a camera of the device, a series of images of the patient in motion, determining respective lengths of the body part in each of the series of images based on comparing the body part in each of the series of images to a skeletal model, and identifying a maximum length of the body part from the respective lengths. The method may include displaying an indication corresponding to the maximum length.

An image annotation method for an image annotation system is provided. The image annotation method includes the following steps. Firstly, an original image is provided. Then, an image pre-processing process is performed on the original image to generate an adjusted image. Then, the adjusted image is inferred according to a deep learning model, so that at least one predicted result is obtained. Then, an image post-processing process is performed on the adjusted image and the at least one predicted result to generate a final image. Then, the final image, the at least one predicted result and at least one annotation of the at least one predicted result are displayed.

The present invention relates to a method for generating three-dimensional image from two-dimensional images, and more specifically, a method for generating three-dimensional image of human bones from two 2D planar images thereof. The method comprises the steps of: providing a first X-ray planar image and a second X-ray planar image; predicting one set of predicted posture parameters for each of the first X-ray planar image and the second X-ray planar image; and generating the data of a stereoscopic image according to the first X-ray planar image, the second X-ray planar image, and the predicted posture parameters. The present invention also relates to a method for training an artificial intelligence to perform three-dimensional image generation described above.

Provided is a laser surgical guidance system including a C-arm fluoroscopy (hereinafter, C-arm) to identify a patient's condition and support a surgical plan and a laser targeting projector to project a line of the surgical plan directly onto an affected part through a line projection module which generates a laser. The laser surgical guidance system may generate a particular laser pattern from the line projection module, transmit the particular laser pattern outputted from the line projection module through a calibration tool including a collimator having a particular orientation, calculate an extrinsic parameter of the calibration tool in a projection image having passed through the calibration tool, and convert coordinates of the C-arm image into the line projection module coordinates using the extrinsic parameter.

The invention provides methods and apparatus for image processing that perform image segmentation on data sets in two- and/or three-dimensions so as to resolve structures that have the same or similar grey values (and that would otherwise render with the same or similar intensity values) and that, thereby, facilitate visualization and processing of those data sets.

The present invention relates to an apparatus (10) for determining an orientation of a patients chest. The apparatus comprises: an input unit (20); and a processing unit (30). The input unit is configured to receive an image of a patient, the image comprising image data of the patients chest. The input unit is configured to receive an X-ray radiograph of the patient's chest acquired by an X-ray imaging unit with an X-ray imaging axis extending from an X-ray source to an X-ray detector. The input unit is configured to provide the image and the X-ray radiograph to the processing unit. The processing unit is configured to determine an orientation of the patients chest in the X-ray radiograph with respect to the X-ray imaging axis, the determination comprising utilization of the image and the X-ray radiograph.