Equipping radiologists with software developed using machine-learning algorithms has led to significantly more cancerous structures or nodules being detected than via human-only readings in clinical studies. Daniel Drieling MeVis Medical Solutions examines the increasingly important support role of artificial intelligence
Being the number one cancer globally, across populations of men and women combined, lung cancer is notoriously challenging to spot sufficiently early to enable positive treatment outcomes. In 2018, lung cancer accounted for 2.09 million of cancer cases globally, and some 1.76 million deaths – more than twice the number of the next biggest killer cancer (colorectal), according to the World Health Organization.1
The problem is that symptoms of lung cancer tend to occur predominantly in the late stages of the disease, when successful treatment becomes more and more difficult. Unlike various other types of cancer, such as breast cancer (accounting for 627,000 deaths in 2018), which can be checked for in a number of different ways, lung cancer requires targeted medical imaging to determine what’s going on.
Better targeting of screening
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