Last updated February 5, 2022 at 04:23 AM
A laser imaging system combined with an artificial intelligence algorithm made it possible to precisely identify brain tumors.
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Published in Nature Medicine on January 6, the study examined the diagnostic accuracy of brain tumor image classification compared to the accuracy of pathological interpretation of conventional histological images. The image classification diagnosis is done using a machine learning computer program. The results for the two methods were almost identical: the diagnosis based on theartificial intelligence was 94,6% accurate, compared to 93,9% for the pathologist-based interpretation.
The imaging technique, stimulated by Raman Histology (SRH), reveals tumor infiltration in human tissue by collecting the scattered laser light, illuminating essential features that are usually not seen in standard histological images.
The microscopic images are then processed and analyzed with artificial intelligence, and in less than two and a half minutes, surgeons can see a predicted brain tumor diagnosis. Using the same technology, after resection, they are able to accurately detect and remove an otherwise undetectable tumor.
“As surgeons, we can only act on what we can see; this technology allows us to see what would otherwise be invisible, improve speed and accuracy in the operating room, and reduce the risk of misdiagnosis, ”says lead author Daniel A. Orringer, MD, associate professor of Neurosurgery at NYU Grossman School of Medicine, who helped develop SSR and co-led the study with colleagues at the University of Michigan. "With this imaging technology, cancer operations are safer and more efficient than ever."
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