Stanford researchers have developed a set of intervention videos to improve mindsets about osteoarthritis and exercise, which was proven in a randomized clinical trial to increase physical activity levels and overall health and wellbeing in an individual.
Stanford researchers have developed easyBAT, a simplified solution integrating a microfluidic sample preparation device with a fully automated analysis pipeline for rapid, accurate and accessible solution for food allergy diagnosis at the point-of-care.
Researchers in the Noh Lab have developed a gait based, emotion recognition system using geophone sensors that are attached to the floor. People's gait changes under various emotions creating distinct structural vibration patterns.
Stanford scientists have developed a novel hydrogel for long-term drug delivery of an Activator Protein 1 (AP-1) inhibitor for the prevention of post-surgical abdominal adhesion.
The Longaker lab at Stanford University has recently discovered that local injection of the drug Verteporfin after wounding can reduce scarring, improve the strength of healed skin, and regrow the hair follicles and sweat glands that are usually lost during the scarring proces
Skin wounds invariably heal by developing fibrotic scar tissue, which can result in devastating disfigurement, growth restriction and permanent functional loss.
Stanford inventors have developed a novel diagnostic tool that identifies distinct immune signatures in the peripheral blood of osteoarthritis patients using mass cytometry (CyTOF) and applied machine learning.
Researchers at Stanford have developed a computational system to robustly generate quantitative perfusion parametric maps automatically from computed tomography (CT) or magnetic resonance (MR) perfusion images.
Stanford BIODESIGN researchers have developed a disease breathalyzer for detecting necrotizing enterocolitis in newborns. Newborn babies face a high risk of blood infections (sepsis) and gastrointestinal inflammation and injury disease (necrotizing enterocolitis 'NEC').
Stanford researchers have invented a unified AI architecture that integrates foundational models (FMs) with AI techniques for efficient analysis of fMRI data in psychiatric disorders.
Stanford researchers have developed a method that allows X-ray and CT imaging to achieve the same signal with two to three orders of magnitude less X-ray dosage.