Intel and Penn Medicine are working on a huge, institution-spanning AI that will help identify brain tumors but without overstepping on strict medical privacy rules. The cross-location AI will use a technique known as "federated learning" as it spans 29 different healthcare and research institutions. Training artificial intelligences with data sets of illnesses, so that they can act as a filter on large numbers of cases, has been shown effective in a number of ways. However the downside is that for the most effective performance those data sets need to be considerable. An individual healthcare institution or research lab would likely struggle to feed a developing machine learning computer with all the information it requires.

An advanced optical imaging approach coupled with an artificial intelligence algorithm has been demonstrated to produce accurate, real-time intraoperative diagnosis of brain tumors, according to a recent study. The study, which has been published in Nature Medicine, examined the diagnostic accuracy of brain tumor image classification through machine learning, which was compared with the accuracy of a pathologist interpretation of histologic images. Researchers reported that the results for both methods were comparable, with the artificial intelligence-based diagnosis being 94.6% accurate and the pathologist-based interpretation being 93.9% accurate. You cannot view this content because It is available to members only. Please Login or Register to view this area.

Analyzing biopsied tissue samples for signs of malignant growth is a crucial part of cancer diagnosis. But many places around the country suffer from a lack of neuropathologists, which prevents these fundamental procedures from happening in a timely manner. Recent studies have warned of a "pathologist gap" which may grow through 2030. Scientists have demonstrated an A.I. not only capable of completing such procedures in less than three minutes, but can do so more accurately than a human. Of the 15.2 million people around the world diagnosed annually with some form of cancer, nearly 80 percent will undergo biopsy surgery to remove and test a piece of the tumor. In the case of brain tumors, which this new study focused on, the testing process can take up to 30 minutes (if there's a neuropathologist on hand to conduct the test) and requires time and labor-intensive freezing, thawing and chemical staining of samples in order to make a diagnosis.

A new study has found that a new system of combining advanced optical imaging with an artificial intelligence algorithm produces an accurate, real-time intraoperative diagnosis of brain tumours. After testing the system, the AI-based diagnosis was found to be 94.6% accurate, while the pathologist-based interpretation was found to be 93.9%

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.

One of the areas in which machine learning has been enjoying success is image recognition. Now, researchers have begun to use machine learning to analyze brain tumors. Primary brain tumors include a broad range that depends on cell type, aggressiveness, and development stage. Being able to rapidly identify and characterize the tumor is vital for creating a treatment plan. Normally, this is a job for radiologists who work with the surgical team; however, in the near future, machine learning will play an increasing role.