A primary brain tumor is present in about 700,000 Americans, as reported by the National Brain Tumor Society. In the future, a newly created artificial intelligence may be used to detect and cure brain tumors.
An AI-based diagnostic screening system dubbed DeepGlioma has been created by neurosurgeons and engineers at the University of Michigan Medical.
Rapid imaging is used by the system to examine tumor samples taken during an operation in progress. The device can successfully identify genetic alterations in brain tumors in less than 90 seconds.
There is great promise in the development of a new AI-based tool that can improve the access and speed of diagnosis and treatment of patients with deadly brain tumors. DeepGlioma presents an outlet for accurate and more prompt detection that would give doctors a greater chance to define therapies and predict patient prognosis.
Gliomas are brain tumors that develop from glial cells. It is estimated that nearly 33% of all brain tumors are gliomas, according to John Hopkin's Medicine. Even though gliomas can take many forms, the new AI study examines diffuse gliomas specifically.
Gliomas that surround brain tissue via indirect infiltration are known as diffuse gliomas. Astrocytomas, oligodendrogliomas, and mixed oligoastrocytomas constitute the most common forms of diffuse gliomas.
There were 153 diffuse glioma patients in the recent AI research, which was released in Nature Medicine on March 23. Model training was placed at the University of Michigan, while patient enrollment sites were located at New York University, the Medical University of Vienna, and University Hospital Cologne.
To anticipate the molecular genetic characteristics required by the WHO to identify diffuse glioma, researchers coupled stimulated Raman histology with deep learning-based picture categorization. Stimulated Raman histology was created by the University of Michigan researchers in 2019 to visualize brain tumor tissue quickly.
