Methods and systems for monitoring a patient's position using non-contact patient monitoring systems are described. In some embodiments, depth sensing cameras are used to obtain depth images of a patient, wherein data can be extracted from the depth images and processed in various ways to make a determination as to the patient's position, including monitoring when and how often a patient changes position. Various alarms systems can also be integrated into the systems and methods described herein in order to alert clinicians regarding, for example, patients moving too infrequently, patients moving too frequently, or patients moving into undesirable positions in view of the condition being treated. AI-based patient monitoring systems used for determining patient position are also described.

Systems and methods obtain functional connectivity data in the whole brain to detect and predict brain disorders. This whole brain data is regionalized and then manipulated to derive functional connectivity data sets that can be used to show measured functional connectivity changes. This whole brain data may also be analyzed to determine changes in functional activity in both increased and decreased neural network connectivity. By identifying and then quantifying the functional connectivity differences between healthy and diseased subjects, a classification for individual subjects can be made.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain feature values related to the shape and a property of the heart. The processing circuitry is configured to estimate, by using the feature values, a state of the heart after a chest is opened. The processing circuitry is configured to estimate, based on a result of the estimation of the state of the heart, a state of the heart after the chest is closed after a treatment is completed. The processing circuitry is configured to output a parameter related to the estimated state of the heart after the chest is closed to a display.

A medical image processing device includes circuitry configured to: analyze a blood flow flowing in an observation object based on a medical observation image obtained by capturing an image of the observation object; and generate a difference result between a simulation result of a blood flow flowing in a 3D model acquired in advance for the observation object and an analysis result of a blood flow flowing in the observation object.

Kits, diagnostic systems and methods are provided, which measure the distribution of colors of skin features by comparison to calibrated colors which are co-imaged with the skin feature. The colors on the calibration template (calibrator) are selected to represent the expected range of feature colors under various illumination and capturing conditions. The calibrator may also comprise features with different forms and size for calibrating geometric parameters of the skin features in the captured images. Measurements may be enhanced by monitoring over time changes in the distribution of colors, by measuring two and three dimensional geometrical parameters of the skin feature and by associating the data with medical diagnostic parameters. Thus, simple means for skin diagnosis and monitoring are provided which simplify and improve current dermatologic diagnostic procedures.

Implementations relate to crop yield prediction at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that capture a first geographic area and are acquired over a first predetermined time interval while the first geographic area includes a particular crop. A first plurality of other data points may also be obtained that influence a ground truth crop yield of the first geographic area after the first predetermined time interval. The first plurality of other data points may be grouped into temporal chunks corresponding temporally with respective images of the first temporal sequence. The first temporal sequence and the temporal chunks of the first plurality of other data points may be applied, e.g., iteratively, as input across a machine learning model to estimate a crop yield of the first geographic area at the end of the first predetermined time interval.

The present invention relates to localizing stenoses. In order to provide improved and facilitated stenosis localization, a device (10) for localizing a stenosis in an angiogram is provided. The device comprises an image supply (12), a data processor (14) and an output (16). The image supply is configured to provide a first image (18) and a second image (20). The first image is an angiographic image that comprises image data representative of a region of interest of a vascular structure in a visible and distinct manner, wherein the vascular structure comprises at least one vessel with at least a part of a stenosis. The second image is a treatment X-ray image that comprises image data representative of at least a part of an interventional device arranged within the vascular structure in a state when the stenosis of the vascular structure is treated. The data processor is configured to identify and delineate the stenosis in the first image based on the first image and at least based on device-related content present in the second image. The data processor is also configured to detect the interventional device in the second image, and to provide a direct identification of structures in the first image that are most similar to the device as detected in the second image. The output is configured to provide an indication of the stenosis.

Vegetation management system includes: a data acquisition unit that acquires input data including remote sensing data obtained by photographing, by remote sensing, a facility and vegetation to be analyzed; a vegetation classification unit that classifies the vegetation photographed in the remote sensing data; a wide-area growth prediction unit that predicts a time-series change of a growth range of the vegetation photographed in the remote sensing data; a vegetation amount simulation unit that predicts a fluctuation in the growth amount of each vegetation by a simulation; a three-dimensional construction unit that constructs a three-dimensional model expressing the facility and the vegetation; and a risk determination unit that determines a contact risk indicating a contact possibility between the facility and the vegetation.

Provided are an intraoral image processing apparatus and an intraoral image processing method. The intraoral image processing method includes obtaining a first intraoral image generated by scanning teeth and a second intraoral image generated by scanning the teeth with an orthodontic device attached thereto, segmenting teeth of the first intraoral image, obtaining a teeth image with the orthodontic device removed therefrom by replacing teeth with the orthodontic device attached thereto included in the second intraoral image with segmented teeth of the first intraoral image, and adjusting teeth of the teeth image by using numbers of teeth.

Provided are systems and methods for determining a change of an abnormality in an anatomical region of a patient based on medical images of a patient. Thereby, a first medical image is acquired at a first instance of time and depicts at least one abnormality in the anatomical region, and a second medical image of the anatomical region of the patient is being acquired at a second instance of time.