For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.

An apparatus for determining a functional index for stenosis assessment of a vessel is provided. The apparatus comprises an input interface (40) and a processing unit (50). The input interface is configured to obtain image data (30) representing a two-dimensional representation of a vessel (6). The processing unit (50) is configured to determine a course of the vessel (6) and a width (w1, w2) of the vessel along its course in the image data and is further configured to determine the functional index for stenosis assessment of the vessel based on the width of the vessel in the image data.

A sensor, such as a photoplethysmography sensor, for non-invasively monitoring a characteristic of an organism, such as a vital body sign. The sensor has multiple light sources disposed on a substrate and an array of optical probing channels for conveying light from the light sources to a probed region. Each detector pixel of an array of detector pixels receives light from a respective optical detection channel after interaction with a subregion of the probed region and spatial filtering, and generates a corresponding pixel signal. A processor derives a value of the vital body sign based at least upon the plurality of pixel signals

Methods, systems, and non-transitory computer readable medium for foot differentiation scoring pertaining to an individual's differences in their two feet. A method includes receiving, from sensors of a scanning device, pressure and/or other measurement data corresponding to feet of a user. The method further includes preprocessing the pressure and/or measurement data. The method further includes generating, based on the preprocessed pressure and/or measurement data, a foot differentiation score based on differences between a left foot and a right foot of the user. The foot differentiation score may assign or correspond to a numerical rating based on how different the user's left foot and right foot are from each other.

Systems and methods for generating a three-dimensional (3D) model of a user's dental arch based on two-dimensional (2D) images of dental impressions include a model training system that provides a machine learning model using training image(s) of a dental impression of a respective dental arch and a 3D training model of the respective dental arch. A model generation system receives first image(s) of a first dental impression of a user's dental arch and second image(s), which may be of the first dental impression or a second dental impression of the dental arch. The model generation system generates a first and second 3D model of the dental arch by applying the first image(s) and second image(s) to the machine learning model.

A method of extracting and displaying postural measurements from patient data includes retrieving, by a processor of a computing device, the patient data from memory. The patient data includes a geometric mesh representation of a patient, including a plurality of data points corresponding to spatial coordinates of a plurality of vertices in three dimensions. The method also includes determining, by the processor, a reference geometry along the geometric mesh representation in a fixed position with respect to the spatial coordinates; determining, by the processor, a landmark corresponding to one of skeletal or soft tissue anatomy for the patient; and determining, by the processor, a postural deviation of a body portion of the patient by comparing the reference geometry and the landmark. The method further includes displaying, by a display of the computing device, a graphical user interface indicating a characteristic related to the postural deviation.

In a method and apparatus for supporting preparation of a patient for a medical imaging investigation, in particular a magnetic resonance investigation, patient data of a patient are acquired by operation of an acquisition unit. An item of position information of an object is calculated by a calculation computer, the calculation of the position information of the object taking place on the basis of the patient data and/or on the basis of an item of investigation information and/or on the basis of data from accessory units. The position information of the object is projected by means of a projection unit.

Presented herein is a method of using a manually-operated light plane generating module to make accurate measurements of the dimensions of an object seen in an image taken by an endoscopic camera. The method comprises: providing the light plane generating module with distinctive features, introducing the light plane generating module until the distinctive features are visible in the image, aligning the light plane across the object, and providing a processor device and software configured to analyze the camera images. Also described are diagnostic or therapeutic endoscopic tools that comprise an attached light plane generating module to provide the tool with integrated light plane measurement capabilities, wherein the tool is configured to be used in the described method.

A performance scanning system that operates to detect and measure surface parameters of a portion of an athlete and uses the surface parameters to determine the likelihood that the athlete's physical performance has been or will be impaired.

A novel multiple camera calibration algorithm uses human joint points for matched key points. A recent machine-learning based human joint detector provides joint positions with labels (e.g. left wrist, right knee, and others). In single person situation, it directly provides matched key points between multiple cameras. Thus, the algorithm does not suffer a key-point matching problem, even in a very sparse camera configuration, which is challenging in the traditional image feature-based method. This algorithm provides easy setup for a multiple camera configuration for marker-less pose estimation.