A system and method is provided for imaging a contrast agent. The system includes a power injector that delivers a contrast agent as a series of boluses using a known period, flow rate, or duration and with a rate of at least one or more separate boluses per cardiac cycle. An x-ray imaging system acquires a reference dataset of the subject before the contrast agent is delivered and acquires an imaging dataset as the series of boluses are delivered to the subject, wherein multiple images are acquired of the subject per bolus. A computer system receives the reference dataset and the imaging dataset from the x-ray imaging system and reconstructs the reference dataset and the imaging dataset using a reconstruction process that removes the subject from the images to generate time-resolved volumetric images of the contrast agent moving within a volume of the subject without the subject.

Provided are a computing device and methods for identifying real-time biometric image. In certain aspects, disclosed a method including the steps of: extracting a first feature information from a nth(n is a natural number) biometric image among biometric images continuously photographed temporally of an object based on a machine learning model; generating a fusion data using at least one sensor data among sensor data temporally corresponding to a n+1th or more biometric images and the first feature information of the nth biometric image; and extracting a second feature information of the n+1th or more biometric images from the fusion data based on a second machine learning model. This present disclosure application is a result developed through the Seoul Industry Promotion Agency's 2021 technology commercialization support project (TB210264), “Improvement and advancement of an explainable artificial intelligence prototype that detects major organs during laparoscopic surgery”.

Disclosed are processes including receiving at least a first and a second image data record corresponding to a first and a second point in time and including a first and a second one or more images of a wound; obtaining an image of the wound from a particular point of view corresponding to the first point in time by analyzing the first image data record; generating a simulated image of the wound from the particular point of view corresponding to the second point in time by analyzing the second image data record; and generating a visual time series view of the wound including at least the image of the wound from the particular point of view corresponding to the first point in time and the simulated image of the wound from the particular point of view corresponding to the second point in time.

Provided are ex vivo systems and methods of predicting the response of a drug or other agent on a tissue. In some embodiments, the systems and methods comprise cutting a tissue into tissue fragments, adding a drug or other agent to the tissue fragments based on an estimated tumor content, and performing an ex vivo measurement on the tissue fragments.

The present invention relates generally to the field of mouthguards. More specifically, the present invention relates to a mouth guard system and method of use. The system is primarily comprised of a kit, at least one mouthguard, a mobile application and a method of use. The mouthguard of the system is preferably manufactured from a rigid, transparent, BPA-free material and can be worn during sleep to correct malocclusion to improve and prevent postural dysfunction, tensions headaches and other body ailments. The method of use further involves using a mold to create a custom mouthguard for a user, wherein the user can then wear the mouthguard and track postural changes induced by the mouthguard via a mobile application.

There is provided a method of processing 2D ultrasound images for computing clinical parameter(s) of a right ventricle (RV), comprising: selecting one 2D ultrasound image of 2D ultrasound images depicting the RV, interpolating an inner contour of an endocardial border of the RV for the selected 2D image, tracking the interpolated inner contour obtained for the one 2D ultrasound image over the 2D images over cardiac cycle(s), computing a RV area of the RV for each respective 2D image according to the tracked interpolated inner contour, identifying a first 2D image depicting an end-diastole (ED) state according to a maximal value of the RV area for the 2D images, and a second 2D US image depicting an end-systole (ES) state according to minimal value of the RV area for the 2D images, and computing clinical parameter(s) of the RV according to the identified first and second 2D images.

Systems and methods for predicting surgical outcomes are provided. A surgical plan comprising information about a planned surgery and at least one preoperative image depicting a planned surgical result and at least one postoperative image depicting an actual surgical result resulting from execution of the planned surgery may be received. The postoperative image may be registered to the preoperative image. One or more features may be automatically identified in each of the postoperative image and the preoperative image. A difference may be automatically measured in at least one parameter of each of the one or more features to yield training data. A function for predicting the difference may be generated using artificial intelligence and based on the training data. The function may be applied to an unexecuted surgical plan.

A method for treatment of a disease by monitoring a progression of the disease includes obtaining image data including a representation of diseased cells of a patient. Based on the type of the disease, one or more features to extract from the image data are determined, the features each representing a physical parameter of at least one of the diseased cells represented in the image data. A feature vector is formed from the extracted features. A machine learning model is selected, and the feature vector is processed using the machine learning model. The machine learning model is trained with labeled image data representing instances of diseased cells having the disease and associating scores representing predicted rates of disease progression with the respective instances of diseased cells having the type of disease. Based on the processing, a score is determined that represents a predicted rate of disease progression indicated by the image data.

Provided are systems and methods for analyzing medical images to localize body regions using deep learning techniques. A combination of convolutional neural network (CNN) and a recurrent neural network (RNN) can be applied to medical images, identifying axial slices of a body region. In accordance with embodiments, boundaries, e.g., superior and inferior boundaries of various body regions in computed tomography images can be automatically demarcated.

Provided herein are methods of assessing the presence of microbes in a liquid sample that include assessing an initial integrated scattering intensity of objects (I.sub.C0) in the sample and an integrated scattering intensity of the objects at a time t (I.sub.Ct) from modified images of the liquid sample, and identifying the sample as comprising microbes for (I.sub.Ct)/(I.sub.C0) above a predefined infection threshold T.sub.I. Related systems and other aspects are also provided.