Multidisciplinary Consortium Sets Sights on Develo

Multidisciplinary Consortium Sets Sights on Developing AI to Detect Gliomas

A new multidisciplinary consortium is combining knowledge from several different disciplines to develop new methods for diagnosing and treating gliomas. Comprised of Sweden’s Umeå University and Umeå University Hospital and PSE Data Security from Switzerland, the consortium will amalgamate knowledge on data security, brain tumors and machine learning to potentially create new and more effective diagnostic and treatment techniques for the brain tumor.

Glioma is a relatively common type of brain tumor that occurs in roughly 33% of brain tumor patients and affects around 19,000 new patients in the United States every year.

Members of the consortium signed a collaboration agreement in a Stockholm meeting last December. They will use anomaly detection models that are effective at combing through massive datasets to help improve early glioma detection. Detecting gliomas in the early stages significantly increases rates of treatment success, but early-stage gliomas can be quite difficult to diagnose.

Heights AI partner Wilfred de Graaf noted that anomaly detection models have been successful at dealing with issues such as detecting money laundering in the financial sector. The anomaly detection works by identifying data points, events or entities that are outside of the normal or expected range and deviate from the standard.

Healthcare provider Region Västerbotten and Umeå University have collected a massive data set of health samples and data from 140,000 patients over the past three decades, which will be critical to the collaboration. Since artificial intelligence (AI) models work more effectively when they have large and high-quality data sets to learn from, this data set will be integral to helping the research team develop novel diagnostic and treatment techniques for gliomas.

Traditional methods of statistical analyses have consistently failed to deliver notable improvements in early glioma detection due to the lack of sufficient data points with predictive value. Many experts in the field colloquially refer to glioma detection as looking for a needle in the haystack thanks to the complexity involved. However, with the advent and proliferation of AI and machine-learning technology over the past decades, researchers are increasingly leveraging the technology to improve healthcare services.

Both PSA Data Security and Heights AI are experts in the nascent artificial intelligence field and have developed AI models that can essentially spot the needle in the haystack much more effectively than previous methods ever could. Umeå University professor of oncology Beatrice Melin notes that the university has a history of translating innovations into clinical applications and said she is happy to be part of a collaboration that will help accelerate the development of new brain tumor detection measures.

When the early detection or diagnosis of gliomas becomes possible, the drugs being developed by entities such as CNS Pharmaceuticals Inc. (NASDAQ: CNSP) could have a heightened chance of delivering desirable clinical outcomes for affected patients.

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