According to the present disclosure, a method of monitoring a patient on an operating room table is provided. The method comprises receiving image data captured by at least one image capture device; and determining a position of the patient relative to the operating table in dependence on the image data. A corresponding system, computer program and non-transitory memory are also provided.

Methods, devices, systems and apparatus for determining emphysema thresholds for processing a pulmonary medical image are provided. In one aspect, a method includes: determining lung lobe regions in the pulmonary medical image, and, for each of the lung lobe regions, clustering CT values in the lung lobe region to divide the lung lobe region into a first sub region and a second sub region and acquiring a CT value corresponding to an intersection of a first CT value distribution function for the first sub region and a second CT value distribution function for the second sub region in the lung lobe region as an emphysema threshold for the lung lobe region.

A method for automatically determining spine deformation from an image showing a number of vertebrae of the spine, comprises: detecting center points of the number of vertebrae shown in the image; constructing a center line based on the center points; computing a local tilt at points along the center line; determining positive and negative tilt-maxima of the local tilt and selecting two reference vertebrae having center points closest to the positive and negative tilt-maxima; segmenting an upper endplate of the cranial reference vertebra; segmenting a lower endplate of the caudal reference vertebra; computing an angle between the upper endplate and the lower endplate; and outputting the angle.

An example computer-implemented method for analyzing skin of a user comprises requesting, via a graphical user interface (GUI), visual media including a face of the user, processing the visual media to generate information for each of a plurality of skin conditions of the face of the user and the information comprises a score per each of a plurality of zones of the face of the user per each of the plurality of skin conditions of the face of the user, mapping the score to a severity level associated with the plurality of skin conditions, and for each of the plurality of skin conditions, the GUI overlaying computer graphics at locations onto the visual media corresponding to the plurality of zones of the face of the user. The computer graphics are depicted with a color associated with the severity level for the skin condition at each of the locations.

A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

A processor divides a fundus region of an ultra-wide field fundus image into plural areas including at least a first area and a second area, generates first attribute information indicating an attribute of the first area and second attribute information indicating an attribute of the second area, and generates first mode instruction information to instruct display of the first area in a first mode corresponding to the first attribute information, and generates second mode instruction information to instruct display of the second area in a second mode corresponding to the second attribute information.

A target surface in an eye is located using a dual aiming beam apparatus that transmits a first aiming beam of light and a second aiming beam of light. An optics subsystem receives a laser beam from a laser source, the first aiming beam of light, and the second aiming beam of light, and directs the beams of light to be incident with the target surface and aligns the beams of light such that they intersect at a point corresponding to a focus of the laser beam. An imaging apparatus captures an image of the target surface including a first spot corresponding to the first aiming beam of light and a second spot corresponding to a second aiming beam of light. A separation between the spots indicates that the focus is away from the target surface, while overlapping spots indicate the focus is at or on the target surface.

A system and method for camera-based stress determination. The method includes: determining a plurality of regions-of-interest (ROIs) of a body part; determining a set of bitplanes in a captured image sequence for each ROI that represent HC changes using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set trained using bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets; determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes; for each ROI, determining intervals between heartbeats based on peaks in the HC change signal; determining heart rate variability using the intervals between heartbeats; determining a stress level using at least one determination of a standard deviation of the heart rate variability; and outputting the stress level.

Systems and methods are provided for evaluating an eye using retinal fluid volumes to provide a clinical parameter. An optical coherence tomography (OCT) image of an eye of a patient is obtained. The OCT image is segmented to produce a total retinal volume and one or both of a subretinal fluid volume and an intraretinal fluid volume for a region of interest within the eye. A metric is generated as a function of the total retinal volume and one or both of the subretinal fluid volume and the intraretinal fluid volume. A clinical parameter for the patient is determined from the metric. The determined clinical parameter is provided to a user at a display.

A computer-implemented method for creating an activity-optimized cutting guides for surgical procedures includes receiving one or more pre-operative images depicting one or more anatomical joints of a patient, and creating a three-dimensional anatomical model of the one or more anatomical joints based on the one or more pre-operative images. One or more patient-specific anatomical measurements are determined based on the three-dimensional anatomical model. A statistical model of joint performance is applied to the patient-specific anatomical measurements to identify one or more cut angles for performing a surgical procedure. A patient-specific cutting guide is created that comprises one or more apertures positioned based on the one or more cut angles.