Stanford researchers have developed an algorithm using deep learning architectures to predict cardiac function (ejection fraction) and trace the endocardium of the left ventricle from ultrasound echocardiogram videos.
Researchers in the Fan group have developed a method for epitaxial growth of double heterojunction semiconductor diodes capable of suppressing parasitic non-radiative recombination effects.
Stanford researchers have developed the first topical regenerative treatment for the oral cavity following chemo/radiation. Approximately 60,000 patients in the U.S. are annually diagnosed with head and neck cancer.
The Bronte-Stewart lab has designed an algorithm for calculating neural activity burst duration to better manage closed loop deep brain stimulation in patients with Parkinson's disease.
Stanford researchers have created a portable, wearable device for long-term nystagmus tracking to better diagnose episodic vertigo. Current methods utilize head goggles in video nystagmography to monitor eye movement while the patient is in a clinical setting.
Radiation therapy is a common option in diseases like breast cancer, but can also cause significant damage to the skin. Permanent scarring and fibrosis can result, with both aesthetic and functional consequences for cancer patients.
Stanford researchers have developed CheXpert which can reduce noise and identify several pathologies on x-rays with very high accuracy via machine learning. CheXpert can read photos of x-rays from a mobile phone and is robust to noise.
Neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) have been characterized by the expansion of the GGGGCC hexanucleotide repeat within the non-coding region of the human chromosome 9 open reading frame 72 (C9ORF72) gene.
Stanford researchers have designed a trainable portable device that can rapidly quantify liver steatosis (fat) prior to transplantation without a pathologist. Currently, rapid assessments are hindered by waiting for an available pathologist to provide results.
Stanford researchers in the laboratory of Dr. Daria Mochly-Rosen have developed novel small molecules for modulating ALDH2 (mitochondrial aldehyde dehydrogenase-2).
Stanford researchers at the Liao and Xing Labs have developed and tested a machine learning algorithm for augmented detection of bladder cancer. Machine learning has the potential to enhance medical decision making in cancer detection and image analysis.
Stanford researchers at the Chichilnisky lab have developed a novel framework for a far superior artificial retina with strikingly near optimal efficiency (96%) of visual perception.