Researchers involved in its development are hopeful that the scanner could eventually lead to improved outcomes in cancer patients who require surgery to remove malignant tissue.
The Learning-based Inference of Longitudinal imAge Changes, or LILAC, system harnesses machine learning to review medical images that have been collected over a prolonged period.
These animals are virtually indestructible and have long outlived the dinosaurs due to their ability to withstand extreme cold, heat, natural disasters and even cosmic radiation.
The algorithm has an edge over standard black box-style artificial intelligence applications because providers are able to see how it reaches conclusions.
Artificial intelligence tools have proven to be beneficial in detecting pulmonary nodules on chest CTs of adults, but less is known about their utility in pediatric populations.
Cardiologists and other physicians have always believed cardiac transthyretin amyloidosis, a progressive heart condition associated with a high mortality rate, was irreversible. Now, though, new evidence suggests that there may be hope.
Motives for the hesitancy are several—transportation concerns, informational inadequacies, historical wrongs—but effective resolutions can be quite simple.
Cedars-Sinai researchers are developing a deep-learning algorithm to personalize patient cardiac risk predictions in a patient-friendly, graphical report.
Someday, getting an MRI exam could be as simple as having food delivered to your door—at least that is the hope of a group of experts at the University of Minnesota who are working on a compact system said to be small enough to sit in the bed of a truck.
