Stanford inventors have created an audio-visual system with a radiotransparent screen provides a means for communication and visual distractions during procedures such as radiation therapy and radiation imaging.
Actigraphy, or the non-invasive study of human activity-rest cycles, is a field of study of growing importance as ambulatory and at-home monitoring of patients becomes more popular.
Researchers at Stanford University have established a deep learning segmentation algorithm for non-contrast CT images to aid clinicians in decision making and improve the speed of symptom to treatment in acute ischemic stroke
Stanford inventors have developed TrueImage, a machine learning algorithm to assess the quality of patient images sent in for telemedicine appointments.
Volumetric contrast-enhanced ultrasound is a new approach to collect 3D imaging data of a contrast signal. So far, analysis of 3D contrast ultrasound has relied on averaging a set of voxels embedded in an ROI or a VOI.
This software tool takes clinical notes from veterinary electronic medical records and assigns SNOMED-CT VET extension diagnostic codes based on the content written on the notes.
Stanford researchers have developed a statistical method to map tissue activity distribution and photon attenuation, correcting for attenuation in real time without a transmission scan, using Positron Emission Tomography.
Stanford researchers have patented a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and non-rigid deformations.
Stanford researchers have patented an automated method for generating articulated human models consisting of both morphological and kinematic model data.
Stanford researchers have developed an Exact Dynamic Collision Checker for Animation and CAD/Robotic Applications that is both infallible and efficient.
Researchers from Stanford University and the Max Planck Institute have patented a new marker-less approach to capturing human performances from multi-view video.