An analysis device configured to analyze an attribute of a correlation between responses with respect to a stimulus to a first subcellular component that is a subcellular component of a cell and a second subcellular component different from the first subcellular component, the analysis device including an attribute analysis unit that analyzes the attribute of the correlation between the first subcellular component and the second subcellular component based on a first change in a feature value of the second subcellular component with respect to the stimulus in a state a function of the first subcellular component is suppressed and a second change in a feature value of the second subcellular component with respect to the stimulus in a state the function of the first subcellular component is not suppressed.

A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.

Provided is a video-based method and system for accurately estimating heart rate and facial blood volume distribution, and the method mainly comprises the following steps: firstly, carrying out face detection of video frame containing human face, and extracting face image sequence and face key position points sequence in time dimension; secondly, compressing these sequence of face image and face key position points to obtain the facial signals in time dimension; thirdly, estimating facial blood volume distribution by facial signals mentioned in third step; finally, estimating heart rate values by using model based on deep learning technology and the spectrum analysis method respectively, then fusing the estimation results by Kalman filter to promote the accuracy of heart rate estimation.

The invention relates to systems and methods to assist physicians in decision making for stroke patients. In particular the systems and methods can be used to assist physicians to decide on whether a patient with an acute ischemic stroke has a large vessel occlusion (LVO) and should be transferred from a community hospital to a larger hospital to undergo an endovascular thrombectomy procedure.

An endoscope system includes: an image acquiring unit that acquires a first frame image obtained by photographing a photographic subject and a second frame image obtained by photographing the photographic subject at a timing different from that of the first frame image; an oxygen saturation calculating unit that calculates an oxygen saturation by using the first frame image and the second frame image; a reliability calculating unit that calculates reliability of the oxygen saturation, calculated by the oxygen calculating unit, by using a signal ratio that is a ratio between a pixel value in a first specific wavelength range corresponding to a specific wavelength range of the first frame image and a pixel value in a second specific wavelength range corresponding to the specific wavelength range of the second frame image; and an information amount adjusting unit that adjusts an information amount of the oxygen saturation by using the reliability.

Described herein are systems and methods for the simulation of the upper airway of a subject. One embodiments provides a method (100) including the initial step (101) of receiving one or more tomographic images of the subject. At step (102) a three dimensional geometric model of the upper airway is generated from the one or more tomographic images. The geometric model includes a network of interconnected deformable mesh elements collectively defining a fluid domain (310) and a solid domain (320). The solid domain (320) defining a single unitary model of the entire upper airway region segmented into a plurality of predefined geometric regions, each being defined by one or more common anatomical parameters. At step (103), a computer simulation is performed on the geometric model to simulate behaviour of the upper airway when the subject is positioned in a predefined position. The computer simulation includes (103a) performing a Computational Fluid Dynamics (CFD) analysis on the fluid domain and then (103b) performing a Fluid-Structure Interaction (FSI) analysis between the fluid and solid domains under the influence of an applied gravity effect. Finally, at step (104), subject-specific parameters are output which are indicative of the behaviour of the upper airway.

A device may obtain first information relating to one or more first lung nodules identified in first imaging of a chest of a patient and second information relating to one or more second lung nodules identified in second imaging of the chest of the patient. The device may provide the first information and the second information to a machine learning model. The device may determine, using the machine learning model, a risk of lung cancer associated with the patient based on an elapsed time between performance of the first imaging and the second imaging and differences between the first information and the second information. The risk of lung cancer may have an inverse correlation to the elapsed time and a direct correlation to the differences. The device may perform one or more actions based on the risk of lung cancer that is determined

Methods, systems, and computer readable media for analyzing an image using machine learning and human computation. A method for analyzing an image includes providing, via multiple instances of an interactive application for analysis of the image, multiple instances, respectively, of the image and receiving, via the interactive application, data from results of analyses of the image including multiple sets of user inputs from the analyses of the multiple instances of the image, respectively. The multiple sets of user inputs are from multiple users, respectively and the multiple users are associated with the multiple instances of the interactive application, respectively. The method further includes processing the received data to identify areas of interest within the image based on the multiple sets of user inputs and analyzing the image using a machine learning algorithm to identify structures in the image based on the identified areas of interest within the image.

A computer implemented method of processing a medical image is disclosed. The method includes receiving a medical image comprising a first plurality of pixels each having an initial pixel value. For each of the first plurality of pixels, a filtering operation is applied to the pixel to generate a filtered pixel value for the pixel based on the initial pixel values of pixels that surround the pixel in the medical image. For each of the first plurality of pixels, a comparison of the initial pixel value with the filtered pixel value is performed. The method comprises, for each of the first plurality of pixels, determining, based on the comparison, whether or not to categorize the pixel as an erroneous pixel; and for each of the first plurality of pixels for which it is determined to categorize the pixel as an erroneous pixel, categorizing the pixel as an erroneous pixel.

A system and method to analyze image data. The image data may be used to assist in determine the presence of a feature in the image. The feature may include a bubble.