An idea that started in 2019, the system combines deep neural networks with an optical imaging method known as stimulated Raman histology, which was also developed at U-M, to image brain tumor tissue in real time. Prior to DeepGlioma, surgeons did not have a method to differentiate diffuse gliomas during surgery. ![]() "Barriers to molecular diagnosis can result in suboptimal care for patients with brain tumors, complicating surgical decision-making and selection of chemoradiation regimens," Hollon said. When it is available, Hollon says, the turnaround time for results can take days, even weeks. However, access to molecular testing for diffuse glioma is limited and not uniformly available at centers that treat patients with brain tumors. ![]() In fact, patients with a specific type of diffuse glioma called astrocytomas can gain an average of five years with complete tumor removal compared to other diffuse glioma subtypes. Molecular classification is increasingly central to the diagnosis and treatment of gliomas, as the benefits and risks of surgery vary among brain tumor patients depending on their genetic makeup. ![]() ![]() The study was published in the journal, 'Nature Medicine' "This AI-based tool has the potential to improve the access and speed of diagnosis and care of patients with deadly brain tumors," said lead author and creator of DeepGlioma Todd Hollon, M.D., a neurosurgeon at University of Michigan Health and assistant professor of neurosurgery at U-M Medical School. In a study of more than 150 patients with diffuse glioma, the most common and deadly primary brain tumor, the newly developed system identified mutations used by the World Health Organization to define molecular subgroups of the condition with an average accuracy over 90 per cent. Washington, March 23 (ANI): Researchers have developed an AI-based diagnostic screening system called DeepGlioma that uses rapid imaging to analyze tumor specimens taken during an operation and detect genetic mutations more rapidly.
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