Malone researchers presented a paper titled “Deep Labeling of fMRI Brain Networks Using Cloud Based Processing” at the 17th International Symposium...... Read More
Machine learning (ML) & artificial intelligence (AI) frameworks are expected to play a significant role in driving innovation and discovery in healthcare research. Examples of ML & AI applications in health care include medical imaging and diagnostics, robot-assisted surgeries, and remote care assistance. To foster research collaborations using ML & AI,...
Epilepsy affects nearly 3.5 million people in the United States and is linked to a five-fold increase in mortality. While epilepsy is often controlled with medication, 20-40% of patients are medically refractory and continue to experience seizures in spite of drug therapies. The alternative for...
Glaucoma is the second leading cause of blindness globally, with approximately 79.6 million people expected to be affected by the disease by 2020. Automated visual field (VF) testing remains the gold standard for identifying patients with glaucoma and judging worsening of disease. Approximately 5% of patients with glaucoma undergo rapid worsening of their VF test.
RAIL is an open structured artificial intelligence focused research collaboration based in the Hopkins Department of Radiology and Radiological Sciences. The group is comprised of physicians and scientists from Johns Hopkins Hospital, the Whiting School of Engineering, and the Applied Physics Laboratory,...
The research team has received funding from the Retina Society to automate the early detection of sickle cell retinopathy....... Read More
Computer engineers and radiologists from the Malone Center for Engineering in Healthcare have teamed up with Microsoft to enhance the AI capabilities...... Read More
Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches...... Read More