Measuring how tumors react to cancer treatment plays a major role in determining a patient’s outcome. The process, normally performed by trained radiologists, is labor-intensive, subjective and prone to inconsistency.
Researchers from Loughborough University, Western General Hospital, the University of Edinburgh, and the Edinburgh Cancer Centre in the United Kingdom, recently developed a deep learning-based method that can analyze compounds in the human breath
Researchers at the National Institutes of Health (NIH), led by visting fellow Dakai Jin, developed a deep learning-based system to automatically inpaint and detect pulmonary nodules, which are round or oval-shaped growths in the lung.
Sixty to seventy million people in the U.S. suffer from gastrointestinal diseases and the best way to clinically diagnose the exact problem is to perform an abdominal ultrasound. However, the process is labor intensive and sometimes inefficient.
A team of researchers from some of the top medical institutions in the world, developed a fully automatic deep learning-based system to detect multiple sclerosis (MS) lesions in the spinal cord and intramedullary from conventional MRI data.
BrainQ is an Israeli-based AI startup that focuses on helping stroke and spinal cord injury patients receive new and innovative treatment, including a deep learning-based system that analyzes a patient’s brain waves and generates a precise treatme
