Published in Nature Medicine, the study showed that AI was faster and more accurate at detecting brain tumour tissue.
Reported in The Independent, “the machine-learning technology was marginally more accurate than a traditional diagnosis made by a pathologist, by just 1 per cent but the results were available in less than two minutes and 30 seconds, compared with 20 to 30 minutes by a pathologist.”
In this latest study, researchers at New York University’s School of Medicine and Langone Hospital used the light from lasers scattered by tumour tissue to identify cancer.
They trained a computer neural network using more than 2.5 million samples from 415 patients split into 13 categories to represent common brain tumours such as malignant glioma, lymphoma, metastatic tumours and meningioma.
Using 278 patients undergoing brain surgery at three medical centres, they split the patients’ specimens and randomly assigned them to either a traditional pathology lab process or the experimental AI carried out during surgery.
The researchers noted the errors in both groups were different, suggesting the potential to achieve 100 per cent accuracy if a pathologist combined with AI examined samples.
Daniel Orringer, an associate professor of neurosurgery at NYU’s Grossman School of Medicine and a senior author, said: “As surgeons, we’re limited to acting on what we can see; this technology allows us to see what would otherwise be invisible to improve speed and accuracy in the [operating theatre] and reduce the risk of misdiagnosis.
“With this imaging technology, cancer operations are safer and more effective than ever before.”
Dr Orringer developed the imaging technology used by the AI, known as stimulated Raman histology or SRH. This allows features not normally seen in tissue samples to be illuminated, meaning it can identify tumour tissue more easily.