A method and a device for determining a presence of tumor are provided. The method includes receiving a medical image associated with a patient. The medical image includes a region of interest associated with the patient. The method includes identifying one or more blood vessels associated with the region of interest in the medical image. The method includes determining a set of characteristics associated with the one or more blood vessels using a trained machine learning model. The method also includes determining whether the one or more blood vessels are feeder vessels associated with the tumor based on the set of characteristics associated with the one or more blood vessels. The method includes detecting a tumor region in the region of interest based on the feeder vessels, when the one or more blood vessels are the feeder vessels associated with the tumor.

The invention proposes a new method for monitoring a health event in a mammal, comprising detecting at least one abnormal value within a series of values related to at least one biomarker, based on appropriate Z-scores.

A method of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification comprises providing a multilayer convolutional network for neurological tumor identification configured for segmenting data sets of full neurologic scans into resolution voxels; supervised learning and validation of the platform by classification of tissue within classification voxels of a specific given training and validation data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of the training and validation data sets having a predetermined ground truth; and implementing the platform by classification of tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of each data set assigned a label. The platform may be used for T-cell therapy initiation and tracking. An artificial intelligence based neuroradiology platform implemented according to the method is disclosed.

An image display method executed by a processor comprises displaying a screen including a two-dimensional fundus image of an examined eye and a three-dimensional eyeball image of the examined eye, finding a second region in the three-dimensional eyeball image that corresponds to a first region specified in the two-dimensional fundus image, and displaying a mark indicating the second region in the three-dimensional eyeball image.

A laser based vascular illumination system utilizing a FPGA for detecting vascular positions, processing an image of such vasculature positions, and projecting the image thereof onto the body of a patient.

The present invention provides an image display method with which a structure of an imaging object can be easily understood on the basis of volume data. An image display method according to an aspect of the present invention includes obtaining photoacoustic image data, generating a first photoacoustic image corresponding to a first spatial region on the basis of photoacoustic image data and a second photoacoustic image corresponding to a second spatial region having a different thickness in a viewing direction of rendering from a thickness of the first spatial region and having a spatial region overlapped with the first spatial region on the basis of the photoacoustic image data, and generating and displaying a parallel image in which the first photoacoustic image and the second photoacoustic image are arranged side by side.

Provided are a method, a computer program and a device for noninvasively evaluating a state of a tear fluid and a tear fluid amount of a tear meniscus. Included are a binarization step of binarizing a tear meniscus image, obtained by capturing at least a part of a tear meniscus of a subject, using a predetermined threshold value; an extraction step of extracting a high luminance region indicating a tear meniscus part from the binarized image; and an evaluation step of evaluating a tear fluid state on the basis of the high luminance region.

The stress estimation device acquires the awakening degree of the subject and calculates the feature amount of the acquired awakening degree. The feature amount of the awakening degree is, for example, a ratio at which the temporal change of the awakening degree is within a predetermined range, information defining a histogram showing the distribution of the temporal change of the awakening degree, and the like. Then, the stress estimation device estimates the stress from the calculated feature amount using the stress model.

There is provided a system for mixed imaging modalities including impedance based analysis of a body portion of a patient, comprising: multi conductor busbar(s), each connected to a controller and at least two of multiple sensing components, a controller arranged to: iteratively perform: sequentially activate as a current source, a first sensing component previously un-used as the current source in earlier iterations, sequentially activating as a current sink, a second sensing component previously un-used as the current sink in earlier iterations, obtain surface voltages, by sequentially activating each of the other sensing components as a respective voltage sensor, and obtaining a respective voltage reading while alternating current is transmitted between the first and second sensing components, wherein the voltages and current obtained for each pair of first and second sensing component of each iteration are provided for computation of a 3D dataset of 3D impedance values of the body portion.

Systems and methods for assessing fluids from a patient are disclosed. The system includes a receptacle including an inlet port, an outlet port, and a third port; a valve system in fluidic communication with the receptacle; and one or more features in the receptacle to aid in optical imaging of fluids. The system has a fill mode and a flush mode. In the fill mode, the valve system directs suction from a vacuum source through the third port into the receptacle, thereby drawing fluid through the inlet port into the receptacle. In the flush mode, the valve system directs suction from the vacuum source through the outlet port, thereby drawing fluid through the outlet port out of the receptacle. Fluid-related information such as, for example, concentration of a blood component, may be estimated based on images of fluids in the receptacle.