A method is provided herein that discloses receiving, by a processor, first image data comprising first bioluminescence data derived from an organism at a first time, receiving, by the processor, second image data comprising second bioluminescence data derived from an organism at a second time, comparing, by the processor, the first image data to the second image data, determining, by the processor, whether a peak light output event has occurred based on the comparison, and outputting, by the processor and to a display device, an indication that the peak light output event has occurred.

The present technology relates to the field of medical monitoring. Patient monitoring systems and associated devices, methods, and computer readable media are described. In some embodiments, a patient monitoring system includes one or more sensors configured to capture first data related to a patient and a monitoring device configured to receive the first data. In these and other embodiments, the patient monitoring system can include an image capture device configured to capture second data related to the patient. In these and still other embodiments, the one or more sensors can be configured to instruct the patient monitoring system to display the second data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Various methods and systems are provided for a medical imaging system. In one embodiment, a method includes acquiring a series of medical images of a lung, identifying a pleural line in each medical image of the series, evaluating the pleural line for irregularities in each medical image of the series, and outputting an annotated version of each medical image of the series, the annotated version including visual markers for healthy pleura and irregular pleura. In this way, an operator of the medical imaging system may be alerted to pleural irregularities during a scan.

A display method applied to a monitor is disclosed. The method includes acquiring a first partition configuration on a display interface of a touch display screen of the monitor; displaying at least one physiological data in the first physiological data display area; displaying data other than the at least one physiological data in the first function information display area; detecting a touch screen operation received by the touch display screen; acquiring a second partition configuration of the display interface if a first touch screen operation that meets a preset re-partitioning rule is detected, the second partition configuration including a second physiological data display area, a second function information display area, and a third function information display area; partially or fully displaying at least one physiological data in the second physiological data display area; and displaying data other than the at least one physiological data in the second function information display area.

A plurality of electrophysiological signals measured by a respective plurality of electrodes carried by a multi-dimensional catheter can be sorted relative to a direction of interest, such as a cardiac activation wavefront direction. An electroanatomical mapping system can be used to determine the orientation of the multi-dimensional catheter relative to the direction of interest. For example, the user can manually adjust the orientation by manipulating a slider, a wheel, or a similar graphical user interface control. As another example, the user can draw a presumed orientation on a geometric model. Once the orientation is determined, the system can sort the plurality of electrophysiological signals and output a graphical representation of the sorted plurality of electrophysiological signals, for example as a plurality of traces.

A system for respiration monitoring includes a garment, which is configured to be fitted snugly around a body of a human subject, and which includes, on at least a portion of the garment that fits around a thorax of the subject, a pattern of light and dark pigments having a high contrast at a near infrared wavelength. A camera head is configured to be mounted in proximity to a bed in which the subject is to be placed, and includes an image sensor and an infrared illumination source, which is configured to illuminate the bed with radiation at the near infrared wavelength, and is configured to transmit a video stream of images of the subject in the bed captured by the image sensor to a processor, which analyzes movement of the pattern in the images in order to detect a respiratory motion of the thorax.

An apparatus for a precise analysis of a severity of arthritis includes an image collection unit configured to collect a medical image having captured a joint of a user, a region detection unit configured to detect one or more regions of interest for analyzing arthritis in the medical image through a learned automatic region detection model, an individual analysis unit configured to extract quantitative feature values from the detected regions of interest and derives one or more individual analysis data from among a severity of arthritis, a severity of osteoproliferation, and a severity of hardness of a subchondral bone based on the feature values, and an integrated analysis unit configured to finely classify a severity of degenerative arthritis through an integrated analysis model learned based on the individual analysis data.

The disclosure purposes to provide an optical coherence tomography (OCT)-based system for diagnosing a high risk lesion such as a vulnerable atheromatous plaque by using an artificial intelligence model through deep learning. A deep learning-based diagnostic method of diagnosing a high risk lesion of a coronary artery includes: acquiring an OCT image of a coronary artery lesion of a patient; extracting a first feature of a thin cap from the OCT image; setting a region of interest included in the OCT image on a basis of the first feature; and determining whether the region of interest includes a high risk lesion.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing EEG source localization. One of the methods includes obtaining brain data comprising: EEG data comprising respective channel data corresponding to each of a plurality of electrodes of an EEG sensor, and fMRI data comprising respective voxel data corresponding to each of a plurality of voxels; identifying, in a three-dimensional coordinate system, a respective location for each electrode; generating, using the respective identified locations of each electrode, data representing a location in the three-dimensional coordinate system of each voxel; determining, for each electrode, a region of interest in the three-dimensional coordinate system; and identifying, for each electrode, one or more corresponding parcellations in the brain of the subject, wherein each parcellation that corresponds to an electrode at least partially overlaps with the region of interest of the electrode.