DEEP TECH HEALTH + NEUROTECH
Sheng Xu and colleagues have developed a wearable, stretchable device that non-invasively evaluates the stiffness of human tissue, at an improved penetration depth, and for a longer period than, existing methods. An ultrasonic array facilitates serial, non-invasive, three-dimensional imaging of tissues, four centimeters below the surface of human skin, at a spatial resolution of 0.5 […]
The mammography screening paradigm has not changed since the 1960s. Breast screening AI company Vara, with Essen University and Memorial Sloan Kettering hospitals, published a study showing that radiologists assisted by AI are better able to screen for breast cancer. The hope is that AI systems could detect cancers that doctors miss, provide better care […]
Mijin Kim and Daniel Heller of the Nanomedicine Lab at Memorial Sloan Kettering Cancer Center have developed an array of carbon nanotube sensors that can “sniff” cancer using AI. The human nose can detect a trillion different scents, through hundreds of olfactory receptors. The pattern which odor molecules bind to which receptors creates a kind […]
Connie Lehman — Professor, Radiology, Harvard Medical School; Chief of Breast Imaging, radiology, Massachusetts General Hospital; Co-director of AVON Breast Center, Radiology, Massachusetts General Hospital; and Director, Breast Imaging Research Center, Radiology, Massachusetts General Hospital spoke at ApplySci’s recent conference at Harvard Medical School. Click to listen to her talk on using AI to discover breast cancer 5 years […]
Stanford’s Sanjiv Gambhir has developed an imaging molecule that can identify pancreatic, cervical and lung cancer early– and, surprisigly, hard-to-detect idiopathic pulmonary fibrosis. The tracer clings to integrin alpha-v beta-6. In a PET scan, the tracer glows due to radioactivity emissions, which allows doctors to see exactly where the tracer is docked in the body. A […]
Stanford’s Hongjie Dai has developed a deep-tissue imaging technique that clearly illuminates tumors beneath the skin. It relies on nanoparticles containing erbium, which glows in the infrared. The promising technology has only been tested on mice, so far. In a study, the technique was used to predict cancer patient response to immunotherapy, and to measure drug […]
CompCyst is a proof-of-concept study, led by Anne Marie Lennon at Johns Hopkins, which uses AI to more accurately determine which pancreatic cysts will become cancerous. The test evaluates molecular and clinical markers in cyst fluids, and could significantly improve detection rates vs. current clinical and imaging tests. In the study, the researchers evaluated molecular profiles, […]
MIT CSAIL professor Regina Barzilay and Harvard/MGH professor Constance Lehman have developed a deep learning model that can predict breast cancer, from a mammogram, up to five years in the future. The model learned subtle breast tissue patterns that lead to malignant tumors from mammograms and known outcomes of 90,000 MGH patients. The goal is […]
Luis Parada and Sloan Kettering colleagues are focusing on cancer stem cells as a new approach to glioblastoma. Like normal stem cells, cancer stem cells have the ability to rebuild a tumor, even after most of it has been removed, leading to cancer relapse and metastasis. According to Parada: “The pharmaceutical industry has traditionally used […]
Duke’s Ravi Bellamkonda has developed a “Tumor Monorail” which tricks aggressive brain tumors such as glioblastoma into migrating into an external container rather than throughout the brain. It has been designated “Breakthrough Device” by the U.S. Food and Drug Administration (FDA). The device mimics the physical properties of the brain’s white matter to entice aggressive tumors to […]