The present disclosure relates to systems and methods for scanning a patient in an imaging system. The imaging system may include at least one camera directed at the patient. The systems and methods may obtain a plurality of images of the patient that are captured by the at least one camera. Each of the plurality of images may correspond to one of a series of time points. The systems and methods may also determine a motion of the patient over the series of time points based on the plurality of images of the patient. The systems and methods may further determine whether the patient is ready for scan based on the motion of the patient, and generate control information of the imaging system for scanning the patient in response to determining that the patient is ready for scan.

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.

A non-contact facial blood pressure measurement method based on 3D CNN is disclosed, which belongs to the technical field of computer vision. The method includes the following steps. S110: collecting an actual face video sample and training a blood pressure prediction model based on face images using 3D CNN neural network. S120: obtaining a face video in real time through a HD camera. S130: recognizing face key points in the face video obtained in S120 through dlib face recognition model, selecting a face region of interest, and extracting face images from the region. S140: performing a wavelet transform operation on the face images extracted in S130 to remove noise. S150: inputting seven consecutive frames of the face images into the 3D CNN blood pressure prediction model trained in S110 to obtain a blood pressure value of the measured person. The disclosure realizes non-contact facial blood pressure measurement.

An imaging unit of an MRI apparatus performs imaging of a positioning image of a subject including a spine; a first imaging that images a cross section including the spine and extending along a longitudinal direction of the spine; and a second imaging that images a cross section in a direction of traversing the spine. An automatic cross-section position setting unit detects a specific tissue of the spine using a scout image or an image including the spine acquired in the first imaging step, performs a matching process between the detected specific tissue of the spine and a spine model, and calculates an imaging cross-section position of the second imaging based upon a specific tissue position of the spine specified by matching, thereby performing automatic setting.

A healthcare apparatus includes a ballistocardiogram (BCG) sensor configured to sense a ballistocardiogram signal of a subject, a camera configured to acquire a color facial image, and a processor configured to detect a region of interest (ROI) from the color facial image, to detect a first color image of a forehead area to acquire a first black and white image, to detect a second color image of a cheek area to acquire a second black and white image, to apply the first and second black and white images to a predetermined trained algorithm model to output a remote photoplethysmography (rPPG) signal waveform of the subject, to calculate a first heart rate from the BCG signal waveform, to calculate a second heart rate from the remote PPG signal waveform, and to output a heart rate of the subject based on the first heart rate and the second heart rate.

Systems, methods, apparatuses, and computer program products for contactless image-based blood oxygen estimation. A method may include receiving an image or video of a part of a subject captured by a camera of a computing device. The method may also include extracting a region of interest of the part of the subject from the image or video. The method may further include performing feature extraction of the region of interest. In addition, the method may include estimating a blood oxygen saturation level of the subject based on a spatial and temporal data analysis of more than two color channels. Feature extraction and estimation of the blood oxygen saturation level may include implementing a combination of spatial averaging, color channel mixing, and temporal trend analysis.

A method for obtaining images for assessing oral health using a mobile device (103) having a screen (205) on one side of a housing of the mobile device (103) and a camera (207) on the opposite side of the housing (103), comprising: —on the mobile device (103) performing a dialogue with a user (101) of the mobile device (103), —determining an area of the user's oral cavity with symptoms based on the dialogue, —instructing the user (101) to position him-/herself in front of a minor (102) for capturing an image of the area of the user's oral cavity with symptoms, —capturing at least one image of the area of the user's oral cavity with symptoms with the camera (207) as instructed, and—either processing the at least one image locally on the mobile device (103), or—transmitting the at least one image or a representation thereof to a remote server (409).

The present disclosure is directed to systems and methods of multiphase pseudo-continuous arterial spin labeling.

The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.

Radiomic analysis of multiparametric magnetic resonance imaging (“MRI”) and magnetic resonance fingerprinting (“MRF”) data enhances delineation and mapping of tumor regions. Radiomic features are extracted from MRI and MRF tumor images. Distinct tumor regions, including but not limited to necrotic core, enhancing tumor, and peritumoral white matter, are segmented and mapped. Whole tumor as well as tumor region characteristics are evaluated. Tumors can also be differentiated and classified by pathology, grading, staging, and so on. Tumor infiltration into peritumoral white matter regions can also be mapped for recurrence prediction