Neurosurgeons may leave the operating room more confident now than ever before about their patient’s brain tumor analysis, thanks to integration with a new system that will allow them to quickly see diagnostic tissue and tumor margins in the near-real time.
The precision and accuracy are only going to continue to progress as they work toward integrating deep learning and computer vision that will make the process quicker say surgeons at University of Michigan Medicine.
Faster also means more affordable.
This means neuropathologists can review the images without the necessity for a pathology lab, eliminating the lengthy wait time required for conventional processing, staining and interpretation.
The researchers also used an artificial intelligence algorithm referred to as deep convolutional neural networks to learn the characteristics of the 10 most common types of brain cancer and forecast diagnosis. Surgeons are provided with a diagnostic forecast over minutes at the bedside with precision similar to that of the traditional method.
“This is the first prospective trial evaluating the use of artificial intelligence in the operating room,” says Hollon, lead author of the publication. “We have executed clinical translation of an AI-based workflow.”
