The present disclosure is directed to methods of predicting overall survival, monitoring, and selecting treatments for a glioblastoma (GBM) patient. The method of the present disclosure includes obtaining at least one morphometric image from the GBM patient, identifying at least one radiomic biomarker based on the at least one morphometric image, and determining an overall survival value based on the at least one radiomic biomarker.

Methods, systems, and coaptation measurement devices as described herein include an elongate sensor body at the end of a proximal connecting member, and a plurality of sensors in an array across a face of the sensor body, wherein each sensor of the plurality of sensors is configured to detect if a portion of a heart valve is in contact with the sensor.

For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.

The present application relates to a method of acquiring a side image for analyzing the degree of ocular proptosis. According to an embodiment, an image acquisition method is provided which is including: acquiring a front image of the subject's face while guidance is

Oven to satisfy a first photographing condition; generating panorama guidance on the basis of position information of a first point and a second point extracted from the front image; providing guidance on movement of a photographing device to acquire a preview image corresponding to the panorama guidance; and acquiring a side image of the subject's face while guidance is given to satisfy a second photographing condition. The first captured image shows iris areas of the subject, and the second captured image shows an outer canthus and a cornea of one of the subject's eyes.

One example discloses a near-field positioning device, including: an input interface configured to receive a set of body-parameters from a user; a controller configured to generate a set of recommended positions for a set of near-field wireless devices to be coupled to the user based on the body-parameters; and an output interface configured to output the recommended positions.

A medicine dispensing device, configured to contain a liquid medicine to be dispensed by the medicine dispensing device and having a plurality of dosing indicia spaced apart from each other around and in relation to a circumference of a surface of the medicine dispensing device, each dosing indicia being of a different height relative to a reference level and corresponding to different dose of the liquid medicine.

A surgical system is provided. The surgical system includes a camera operable to capture images and/or video. A projector is operable to project light, and a controller is communicatively coupled with the camera and the projector. The controller is operable to track movement of bone in real-time during surgery based on the images and/or video captured by the camera, and control the projector to project the light including a cutting line on the bone to indicate a cutting plane for cutting the bone during surgery.

A method and system for deriving one or more hemodynamic parameters based on blood-velocity and arterial diameter measures, or parameters proportional thereto, each sampled recurrently or continuously over a time period to obtain for each a data series spanning a time window (i.e. a waveform). This is used, preferably in combination with at least one further physiological parameter, for example heart rate, to derive one or more hemodynamic parameters. A transfer function or machine learning model is used to process the inputs to obtain the estimated hemodynamic parameters.

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.