Researchers have developed a computer algorithm that they say can analyze digital images of a woman’s cervix and accurately identify precancerous changes that require medical attention. This artificial intelligence approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings.
Led by investigators from the National Institutes of Health and humanitarian tech investment fund Global Good, the researchers used comprehensive datasets to “train” a machine-learning algorithm to recognize patterns in complex visual inputs, such as medical images. The findings were confirmed independently by experts at the National Library of Medicine. The results appeared in the Journal of the National Cancer Institute (NCI).
At DeviceTalks Boston, Tyler Shultz will give attendees an inside look at Theranos and how he was able to sound the alarm after he realized the company was falling apart. Shultz will take attendees behind the story that everyone is talking about: the rise and fall of Elizabeth Holmes and her diagnostic company, Theranos.
Join Shultz and 1,000+ medical device professionals at the 8th annual DeviceTalks Boston.