An X-ray imaging system using multiple pulsed X-ray sources to perform highly efficient and ultrafast 3D radiography is presented. There are multiple pulsed X-ray sources mounted on a structure in motion to form an array of sources. The multiple X-ray sources move simultaneously relative to an object on a pre-defined arc track at a constant speed as a group. Electron beam inside each individual X-ray tube is deflected by magnetic or electrical field to move focal spot a small distance. When focal spot of an X-ray tube beam has a speed that is equal to group speed but with opposite moving direction, the X-ray source and X-ray flat panel detector are activated through an external exposure control unit so that source tube stay momentarily standstill equivalently. 3D scan can cover much wider sweep angle in much shorter time and image analysis can also be done in real-time.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for classifying a patient. In one aspect, a method comprises: receiving multi-modal data characterizing a patient, wherein the multi-modal data comprises a respective feature representation for each of a plurality of modalities; processing the multi-modal data characterizing the patient using an encoder neural network to generate an embedding of the multi-modal data characterizing the patient; determining a respective classification score for each patient category in a set of patient categories based on the embedding of the multi-modal data characterizing the patient; and classifying the patient as being included in a corresponding patient category from the set of patient categories based on the classification scores.

A method for assisting in cell isolation from a biological tissue section includes: obtaining cell images that are each an image of cells isolated from the biological tissue section, the biological tissue section being soaked in a solution containing an enzyme; calculating, from the cell images, a number of the cells isolated from the biological tissue section as an indicator of the cell isolation from the biological tissue section; and visualizing a temporal change in the indicator on the basis of a history of the indicator.

A computing system for wound tracking is disclosed herein. A server computing device receives a first image of a wound of a patient captured by a first camera. Subsequently, the server computing device receives a message generated by a computing device, the message indicating that a second camera of the computing device is to capture a second image of the wound. Responsive to receiving the message, the server computing device causes data to be transmitted to the computing device, the data based in part upon the first image. The data causes the computing device to present a semi-transparent overlay to a view of the wound on a display as perceived through a lens of the second camera, the semi-transparent overlay indicative of the first image. The computing device captures the second image via the second camera and causes the second image to be received by the server computing device.

A method to increase the reliability and clinical utility of diffusion tensor imaging (DTI) of traumatic brain injury (TBI) in single subjects and a semi-automated method of identifying and quantifying small hemorrhages using susceptibility-weighted images (SWI) of single subjects include storing an image template formed from control subjects, storing a brain image of the single subject, correcting for image acquisition differences of the control subjects and single subject, and performing regional analysis of the brain image of the single subject. The method may include analysis of fractional anisotropy values that are age-corrected between the control subjects and the single subject before performing voxel-based analysis (VBA), and a hybrid VBA and tract-based spatial statistical (TBSS) analysis with the VBA and TBSS results combined using a statistical calculation. The resulting combined DTI image may be further combined with an SWI image, FLAIR image, and/or T1 image of the single subject.

Techniques of automated quality control for digital pathology whole slide images are presented. The techniques include obtaining a thumbnail image derived from a whole slide image of a pathology slide; determining whether the whole slide image includes an artifact in a first class of artifacts by providing the thumbnail image to an electronic neural network trained to detect artifacts in the first class of artifacts by analyzing a plurality of labeled training thumbnail images; generating a tissue mask representing tissue depicted in the thumbnail image; determining whether the whole slide image includes an artifact in a second class of artifacts by performing a comparison using the tissue mask; and providing an indication of whether the whole slide image includes an artifact in the first class of artifacts or an artifact in the second class of artifacts.

A stroke detection system analyzes images of a person's face over time to detect asymmetric changes in the position of certain reference points that are consistent with sagging or drooping that may be symptomatic of a stroke or TIA. On detecting possible symptoms of a stroke or TIA, the system may alert caregivers or others, and log the event in a database. Identifying stroke symptoms automatically may enable more rapid intervention, and identifying TIA symptoms may enable diagnostic and preventative care to reduce the risk of a future stroke.

The current document is directed to methods and systems for monitoring a dental patient's progress during a course of treatment. A three-dimensional model of the expected positions of the patient's teeth can be projected, in time, from a three-dimensional model of the patient's teeth prepared prior to beginning the treatment. A digital camera is used to take one or more two-dimensional photographs of the patient's teeth, which are input to a monitoring system. The monitoring system determines virtual-camera parameters for each two-dimensional input image with respect to the time-projected three-dimensional model, uses the determined virtual-camera parameters to generate two-dimensional images from the three-dimensional model, and then compares each input photograph to the corresponding generated two-dimensional image in order to determine how closely the three-dimensional arrangement of the patient's teeth corresponds to the time-projected three-dimensional arrangement.

Methods for characterizing fluids from a patient. A time series of images of a conduit are received, and a conduit image region in the images is identified. A flow type of the fluids passing through the conduit may be classified as one of air, laminar liquid, and turbulent liquid by evaluating an air-liquid boundary of the fluid. A volumetric flow rate of the fluids in the conduit is estimated. The volumetric flow rate may be based on the classified flow type. A concentration of a blood component of the fluids passing through the conduit may be estimated based on the images. A proportion of the fluid that is blood may also be determined, and a volume of blood that has passed through the conduit within a predetermined period of time may be estimated based on the estimated total volumetric flow rate and the determined proportion.

A method for tissue classification includes receiving at least two images associated with a patient, the at least two images being of an anatomical region or tissue. The method also includes identifying a region of interest in the at least two images, analyzing the region of interest to identify changes in the tissue and generating a probability map of the region of interest based on the changes in the tissue. The probability map indicates a likelihood of formation of cancer in the tissue within a predetermined time period. The method also includes displaying the probability map on a display.