Accurate intraoperative diagnosis is essential for providing safe and effective care during brain tumor surgery.

With a deepening focus on unleashing novel applications of artificial intelligence (AI) across--and beyond--neurosurgery, a multidisciplinary team of physicians and mathematicians are collaborating on advanced approaches to diagnosis and patient care, developing data-driven methods that hold potential for progress across the continuum of medicine. Investigations into clinical applications for AI, with a focus on neurosurgical care, have gained significant momentum with the recruitment of Eric K. Oermann, MD, assistant professor in the Departments of Neurosurgery and Radiology and a leading expert in AI applications in medicine. Dr. Oermann brings deep expertise at the intersection of neurosurgery and mathematics to research projects that apply data science and algorithms to answer pressing neurosurgical questions as well as those that apply to medicine far beyond neurosurgery. "Neurosurgery tends to be the technical spearhead of the broader medical world, innovating to benefit our own patients and medicine with a capital M," he says. "So our discoveries in AI are at the next forefront of technological innovation in medicine, writ large." Dr. Oermann developed the vision for his research in close partnership with Daniel A. Orringer, MD, associate professor in the Departments of Neurosurgery and Pathology.

Artificial intelligence (AI) is showing promise in the operating room as new research shows machine-learning can diagnose brain tumors at a fraction of the time it takes expert human pathologists. According to new research published today in the scientific journal Nature Medicine, a new AI system was able to accurately diagnose a brain tumor in two minutes. The traditional method of sending tissue to a lab, freezing and staining it, then examining it through a microscope typically requires about 20 to 30 minutes or longer for pathologists. The AI-based diagnosis was also about as precise as the standard method, with 94.6 percent accuracy compared to conventional human diagnosis of 93.9 percent. The new method streamlines the practice of analyzing tissue samples while the patient is still on the operating table.

PARIS – An AI computer model can diagnose and identify 10 of the most common types of brain cancer with the same accuracy as human doctors, researchers said Monday. More than 15 million people worldwide are diagnosed with cancer every year, and 80 percent of those will undergo surgery. In the United States alone more than 1 million cancer samples are biopsied annually, and each one must be analyzed and diagnosed by a pathologist, placing enormous strain on health services. Writing in the journal Nature Medicine, a team of U.S.-based experts described how they trained an AI algorithm to analyze brain cancers from more than 2.5 million images. They found that the computer was able to diagnose common cancers in under three minutes -- more than 10 times faster than a human expert.

A novel method of combining advanced optical imaging with an artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of brain tumors, a new study finds. Published in Nature Medicine on January 6, the study examined the diagnostic accuracy of brain tumor image classification through machine learning, compared with the accuracy of pathologist interpretation of conventional histologic images. The results for both methods were comparable: the AI-based diagnosis was 94.6% accurate, compared with 93.9% for the pathologist-based interpretation. The imaging technique, stimulated Raman histology (SRH), reveals tumor infiltration in human tissue by collecting scattered laser light, illuminating essential features not typically seen in standard histologic images. The microscopic images are then processed and analyzed with artificial intelligence, and in under two and a half minutes, surgeons are able to see a predicted brain tumor diagnosis.

Published in Nature Medicine on January 6, the study examined the diagnostic accuracy of brain tumor image classification through machine learning, compared with the accuracy of pathologist interpretation of conventional histologic images. The results for both methods were comparable: the AI-based diagnosis was 94.6% accurate, compared with 93.9% for the pathologist-based interpretation. The imaging technique, stimulated Raman histology (SRH), reveals tumor infiltration in human tissue by collecting scattered laser light, illuminating essential features not typically seen in standard histologic images. The microscopic images are then processed and analyzed with artificial intelligence, and in under two and a half minutes, surgeons are able to see a predicted brain tumor diagnosis. Using the same technology, after the resection, they are able to accurately detect and remove otherwise undetectable tumor.

If there's ever a time you want to spend less time under the knife, it's during brain surgery. Artificial intelligence could help doctors diagnose brain tumors more quickly and more accurately, according to a new study by researchers at the University of Michigan Medical School and Harvard University. "Our goal is to develop an algorithm that approaches the performance of a neuropathologist at diagnosis during an operation," said Dr. Daniel Orringer, first author of the study in Nature Biomedical Engineering and an assistant professor of neurosurgery at Michigan Medicine. In their experiments on more than 100 brain tissue samples, the researchers used deep learning to detect the presence of a tumor and classify it into one of several broad categories. The algorithm analyzes tissue from a laser imaging technique the researchers developed called stimulated Raman histology, or SRH.