A physiological detection device includes system including a first array PPG detector, a second array PPG detector, a display and a processing unit. The first array PPG detector is configured to generate a plurality of first PPG signals. The second array PPG detector is configured to generate a plurality of second PPG signals. The display is configured to show a detected result of the physiological detection system. The processing unit is configured to convert the plurality of first PPG signals and the plurality of second PPG signals to a first 3D energy distribution and a second 3D energy distribution, respectively, and control the display to show an alert message.

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.

Information representing a physique of a subject is extracted from an image obtained by imaging the subject. A group in which the subject is classified is specified, using the extracted information representing the physique of the subject. Image data representing a medical image obtained by imaging the subject is input to a learned model corresponding to a specified group among learned models obtained for each group by machine learning using learning data for each group. Information representing an area extracted from the medical image is acquired, which is output from the learned model with the input.

A method for analyzing an abdominal disease based on a medical image, includes receiving and preprocessing a medical image obtained by photographing an abdominal region of a patient to detect a plurality of analysis candidate regions and setting one of the plurality of analysis candidate regions as a ROI, calculating a nodule grade based on surface unevenness of the ROI, calculating a cellular heterogeneity coefficient based on pixel homogeneity of the ROI, and predicting and outputting an abdominal disease value based on the nodule grade and the cellular heterogeneity coefficient.

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.

This specification describes a method of visually displaying electrocardiogram data in a compressed manner on the display screen wherein rhythmic information is visible and a method of categorizing zones of the electrocardiogram data.

The present application discloses a method, device, and system for processing a medical image. The method includes obtaining, by one or more processors, a target image, segmenting, by the one or more processors, a target region image from the target image, wherein the target region image comprises a target object region in the target image, analyzing, by the one or more processors, the target region image based at least in part on a machine learning model, and obtaining by the one or more processors, a recognition result based at least in part on the analysis of the target region image.

A diagnosis support system having an image sensor that captures a medical image and a processor configured to capture medical images of which imaging times are different, store any of medical images as representative images, store the medical images excluding the representative images as groups of neighboring images such that each the groups of neighboring images are associated with the representative images, display the representative images in a list on a display device, receive a selection from the representative images displayed in the list, extract a neighboring image for diagnosis from among a group of neighboring images which is associated with a selected representative image, and display the extracted neighboring image for diagnosis on the display.

A lung region can be appropriately divided with a region dividing device, method, and program for dividing a lung region included in a medical image. A bronchus extracting unit 30 of a region dividing unit 22 extracts a bronchus from a lung region included in a medical image acquired by imaging a subject including a lung. A first dividing unit 31 to a fourth dividing unit 34 identify a plurality of branch positions of the bronchus and divide, based on the branch positions, the lung region into a plurality of regions.

An information processing device includes a skin-region detecting unit, a measurement-region setting unit, a pulse-wave source signal extracting unit, a phase-coincidence-degree calculating unit, and a pulse-wave estimating unit. The skin-region detecting unit detects a skin region of a person in each of multiple frames in a predetermined time period. The measurement-region setting unit sets multiple measurement regions in the skin region. The pulse-wave source signal extracting unit extracts multiple pulse-wave source signals indicating a change in luminance from the multiple measurement regions. The phase-coincidence-degree calculating unit calculates multiple phase coincidence degrees each indicating a degree of phase coincidence between phases of corresponding base components constituting each of the multiple pulse-wave source signals. The pulse-wave estimating unit specifies one of the phase coincidence degrees having the highest degree of phase coincidence and estimates a pulse wave of the person based on the base components corresponding to the specified phase coincidence degree.