Expert human pathologists typically require around 30 minutes to diagnose brain tumors from tissue samples extracted during surgery. A new artificially intelligent system can do it in less than 150 seconds—and it does so more accurately than its human counterparts.
New research published today in Nature Medicine describes a novel diagnostic technique that leverages the power of artificial intelligence with an advanced optical imaging technique. The system can perform rapid and accurate diagnoses of brain tumors in practically real time, while the patient is still on the operating table. In tests, the AI made diagnoses that were slightly more accurate than those made by human pathologists and in a fraction of the time. Excitingly, the new system could be used in settings where expert neurologists aren’t available, and it holds promise as a technique that could diagnose other forms of cancer as well.
New York University neuroscientist Daniel Orringer and his colleagues developed a diagnostic technique that combined a powerful new optical imaging technique, called stimulated Raman histology (SRH), with an artificially intelligent deep neural network. SRH uses scattered laser light to illuminate features not normally seen in standard imaging techniques
To create the deep neural network, the scientists trained the system on 2.5 million images taken from 415 patients. By the end of the training, the AI could categorize tissue into any of 13 common forms of brain tumors, such as malignant glioma, lymphoma, metastatic tumors, diffuse astrocytoma, and meningioma.
A clinical trial involving 278 brain tumor and epilepsy patients and three different medical institutions was then set up to test the efficacy of the system. SRH images were evaluated by either human experts or the AI. Looking at the results, the AI correctly identified the tumor 94.6 percent of the time, while the human neuropathologists were accurate 93.9 percent of the time. Interestingly, the errors made by humans were different than the errors made by the AI. This is actually good news, because it suggests the nature of the AI’s mistakes can be accounted for and corrected in the future, resulting in an even more accurate system, according to the authors.
“SRH will revolutionize the field of neuropathology by improving decision-making during surgery and providing expert-level assessment in the hospitals where trained neuropathologists are not available,” said Matija Snuderl, a co-author of the study and an associate professor at NYU Grossman School of Medicine, in the press release.