An adaptor for configuring a mobile communication device for tissue imaging is disclosed. The adaptor includes a housing configured to be removably coupled to a mobile communication device, at least one excitation light source for fluorescent imaging, a white light source for white light imaging, and a power source for powering the adaptor. The excitation light source is configured to emit light in one of ultraviolet, visible, near-infrared, and infrared ranges.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical feature identification algorithm to identify one or more patient specific anatomical features within the medical images by exploring an anatomical knowledge dataset. A 3D surface mesh model may be generated representing the one or more classified patient specific anatomical features, such that information may be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model may be generated based on the extracted information.

The present invention refers to providing a system that allows to very accurately determine the state of a disease, like COPD, in a patient. The system (100) comprises a unit (101) for providing images of the region of interest corresponding to different states of the region of interest, a unit (102) for elastically registering the images to each other resulting in an elastic registration output, a unit (103) for determining a specific tissue region in an image, a unit (104) for determining a specific elastic registration output for the specific tissue region based on the determined elastic registration output, and a unit (105) for determining an elastic indicator for the specific tissue type based on the specific elastic registration output. Thus, a state of a disease that influences the elastic properties of the specific tissue type can be determined very accurately.

A vehicle control device includes a tracking unit estimating a motion of a moving object, a model selection unit selecting a motion model corresponding to a moving object type, an abnormality determination unit determining a presence or absence of an abnormality of the estimation of the motion of the moving object based on the estimated moving object motion and the motion indicated by the motion model, and a control unit. A control mode in which the control unit controls traveling of a host vehicle when the abnormality determination unit determines that the abnormality is present differs from a control mode in which the control unit controls the traveling of the host vehicle when the abnormality determination unit determines that the abnormality is absent.

Computer-implemented systems and methods link time-stamped OCT scan data of a patient's retina in a database to a biometric identifier for the patient. An appropriately trained artificial intelligence (AI) computer system determines whether the patient has an eye disease based on the differences between time-stamped OCT scan data for the patient from different time scans.

Existing techniques in precision farming comprise supervised event detection and need labeled training data which is tedious considering the large number of crops, differences therein and even larger number of diseases and pests. The present disclosure provides an unsupervised method and uses images of any size captured in an uncontrolled environment. The methods and systems disclosed find application in automatically localizing and classifying events, including health state and growth stage and also estimating an extent of manifestation of the event. Information of spatial continuity in pixels and boundaries in a given image is used to update the feature representation and label assignment to every pixel using a fully convolutional network. Back propagation of the pixel labels modified according to the output of a graph based method helps the neural network to converge and provide a time efficient solution.

Systems, methods, and devices for tracking one or more objects are provided. A three-dimensional representation of one or more objects may be received. The three-dimensional representation may be inputted into a segmentation model configured to segment the three-dimensional representation into one or more segmented objects. One or more segmented objects may be received from the segmentation model. One or more characteristics of at least one of the one or more segmented objects may be tracked.

An information processing apparatus comprising at least one processor, wherein the at least one processor is configured to: acquire a document describing a subject; extract document finding information indicating a finding of the subject included in the document; and specify a finding extraction process for extracting image finding information indicating the finding indicated by the document finding information from a first image obtained by imaging the subject, among a plurality of types of finding extraction processes for extracting image finding information indicating a plurality of different types of findings that are able to be included in the first image.

Embodiments described herein provide methods and systems for predicting a subject response to a therapeutic. The methods and systems generally operate by using a machine learning (ML) component trained using time-series responses and image features to generate time-series responses of a subject to a therapeutic that has been administered to the subject.

Methods and systems for characterizing tissue of a subject are disclosed. The method includes retrieving a time series of angiography images of tissue of a subject, defining a plurality of calculation regions, generating a time-intensity curve for each respective calculation region, calculating a rank value for each respective calculation region based on one or more parameters derived from the time-intensity curve; and generating a viewable image in which on the image position of each calculation region an indication is provided of the calculated rank value for that calculation region. Also disclosed are methods and systems for generating first and second time-intensity curves for respective first and second calculation regions, calculating first and second rank values for the respective calculation regions based on first and second pluralities of parameters selected to approximate the respective time-intensity curves, and generating a spatial map of the first and second calculated rank values.